US8521530B1 - System and method for enhancing a monaural audio signal - Google Patents

System and method for enhancing a monaural audio signal Download PDF

Info

Publication number
US8521530B1
US8521530B1 US12/217,076 US21707608A US8521530B1 US 8521530 B1 US8521530 B1 US 8521530B1 US 21707608 A US21707608 A US 21707608A US 8521530 B1 US8521530 B1 US 8521530B1
Authority
US
United States
Prior art keywords
signal
sub
energy
band
noise
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active, expires
Application number
US12/217,076
Inventor
Mark Every
David Klein
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Knowles Electronics LLC
Original Assignee
Audience LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Audience LLC filed Critical Audience LLC
Priority to US12/217,076 priority Critical patent/US8521530B1/en
Assigned to AUDIENCE, INC. reassignment AUDIENCE, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KLEIN, DAVID, EVERY, MARK
Application granted granted Critical
Publication of US8521530B1 publication Critical patent/US8521530B1/en
Assigned to AUDIENCE LLC reassignment AUDIENCE LLC CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: AUDIENCE, INC.
Assigned to KNOWLES ELECTRONICS, LLC reassignment KNOWLES ELECTRONICS, LLC MERGER (SEE DOCUMENT FOR DETAILS). Assignors: AUDIENCE LLC
Application status is Active legal-status Critical
Adjusted expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208Noise filtering
    • G10L21/0216Noise filtering characterised by the method used for estimating noise
    • G10L21/0232Processing in the frequency domain
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04MTELEPHONIC COMMUNICATION
    • H04M9/00Interconnection arrangements not involving centralised switching
    • H04M9/08Two-way loud-speaking telephone systems with means for suppressing echoes or otherwise conditioning for one or other direction of traffic

Abstract

A method, system, and computer program for enhancing a signal are presented. The signal is received, and energy estimates of the signal may be determined. At least one characteristic of the signal may be inferred based on the energy estimates. A mask may be generated based, in part, on the at least one characteristic. In turn, the mask may be applied to the signal to produce an enhanced signal, which may be outputted.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. patent application Ser. No. 11/343,524, filed Jan. 30, 2006 and entitled “System and Method for Utilizing Inter-Microphone Level Differences for Speech Enhancement,” U.S. patent application Ser. No. 11/825,563, filed Jul. 6, 2007 and entitled “System and Method for Adaptive Intelligent Noise Suppression,” and U.S. patent application Ser. No. 12/072,931, filed Feb. 29, 2008 and entitled “System and Method for Providing Single Microphone Noise Suppression Fallback,” which all are incorporated by reference.

BACKGROUND

1. Technical Field

The present invention generally relates to audio processing. More specifically, the present invention relates to enhancing a monaural audio signal.

2. Description of Related Art

Presently, there are numerous methods for reducing background noise from speech in adverse environments. Many of these methods require multiple microphones. Techniques such as beam-forming, time-difference-of-arrival, inter-microphone level differences, and blind source separation are used to spatially filter signals from the multiple microphones to suppress noise originating from different spatial locations relative to a location of a wanted signal, such as speech.

There are also some methods for using a monaural signal to reducing background noise. For example, a noise suppressor is sometimes incorporated into speech codecs or within a telecommunications network. In these cases, the noise suppressor is commonly used to suppress only stationary or slowly time-varying background noise.

Disadvantageously, in adverse environments containing non-stationary noise, noise leakage and audio artifacts can result when using the noise suppressor. Additionally, noise originating in close proximity to a speech source may not be distinguishable from the speech source using method using the multiple microphones. Furthermore, damage to, blockage of, or mismatch between the multiple microphones may result in suboptimal performance. In these cases, a system using the multiple microphones may revert to a monaural method for noise suppression. As such, it is advantageous to have a system which may provide non-stationary noise suppression using the monaural signal, such as provided by a single microphone.

SUMMARY OF THE INVENTION

A method, system, and computer program for enhancing a signal are presented. The signal may be received by a microphone or as a downlink signal, in accordance with various embodiments. In some embodiments, the signal is converted to a frequency-domain. Energy estimates of the signal may then be determined. According to one embodiment, a speech probability distribution and a noise probability distribution are determined based on the energy estimates.

At least one characteristic of the signal may be inferred based on the energy estimates. In exemplary embodiments, the at least one characteristic may comprise a noise estimate or a signal-to-noise ratio. Various assumptions may be involved in inferring the at least one characteristic. For example, a distribution of speech energy in the signal may be assumed to be different from a distribution of noise energy in the signal. In addition, speech energy in the signal may be assumed to vary more rapidly than noise energy in the signal in various embodiments.

A mask may be generated based, in part, on the at least one characteristic. Generating the mask may include using a pitch estimate in exemplary embodiments. The mask may be applied to the signal to produce an enhanced signal. The enhanced signal may then be outputted, for example, by a speaker or as an uplink signal. According to various embodiments, the enhanced signal may be converted to a time-domain.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an environment in which exemplary embodiments of the present invention may be practiced.

FIG. 2A is a block diagram of an exemplary audio device implementing various embodiments of the present invention.

FIG. 2B is a block diagram of a usage scenario of various exemplary embodiments of the present invention.

FIG. 3 is a block diagram of an exemplary embodiment of an audio processing system.

FIG. 4 is a block diagram of an inference engine according to exemplary embodiments.

FIG. 5 is a block diagram of an exemplary embodiment of the mask generator engine.

FIG. 6 is a flowchart of an exemplary method for enhancing a signal.

DETAILED DESCRIPTION

The present invention provides exemplary systems and methods for enhancing a monaural audio signal. In exemplary embodiments, enhancement (e.g., noise suppression) of the monaural audio signal may be achieved by converting the monaural audio signal from a time-domain to a frequency-domain, applying a mask to the monaural audio signal in the frequency-domain, and converting the signal back to the time-domain to obtain an enhanced signal in the time-domain. In some embodiments, the mask may be generated based, in part, on estimates of a signal-to-noise ratio (SNR) of the monaural audio signal. Additionally, estimates of speech and noise energies of the monaural audio signal may also be a basis for generating the mask in exemplary embodiments. Generation of the mask may be further based, in part, on a pitch estimate, in accordance with various embodiments. In exemplary embodiments, the pitch estimate may be determined from a preliminary noise suppressed signal obtained by applying a preliminary mask to the monaural audio signal. As a result, enhancement of the monaural audio signal may be realized such that wanted signals (e.g., speech) are clear and undistorted, while unwanted signals (e.g., noise) are optimally suppressed. While some embodiments of the present invention may be described in reference to operation on a cellular phone, the present invention may be practiced on any audio device.

FIG. 1 illustrates an environment 100 in which exemplary embodiments of the present invention may be practiced. As depicted, the environment 100 comprises a speech source 102, an audio device 104, and a noise source 106. The audio device 104 includes an input device 108 for receiving the monaural audio signal comprising speech and noise (i.e., a mixed signal comprising both the wanted and the unwanted signals). The audio device 104 will be discussed further in connection with FIG. 2.

According to various embodiments, the speech source 102 may be an individual providing an audio signal, such as speech. However, the speech source 102 may be substituted by any originator of a wanted signal in some embodiments. To illustrate, the wanted signal may comprise a decoded downlink signal, thus making a sender of the downlink signal the originator (e.g., see FIG. 2B).

The noise source 106 may provide stationary, non-stationary, and/or a combination of both stationary and non-stationary noise. The stationary noise may be considered to be relatively constant over time (e.g., car noise, street noise, and babble noise), while the non-stationary noise may be considered to be impulsive or rapidly changing over time (e.g., keyboard typing and music). Although only a single noise source 106 is depicted in FIG. 1, the noise source 106 may comprise any sounds from one or more locations. The noise source 106 may also include echoes and reverberation in accordance with various embodiments.

FIG. 2A is a block diagram of the exemplary audio device 104 implementing various embodiments of the present invention. The audio device 104 may comprise any device configured to receive sound, for example, cellular phones, phone handsets, phone headsets, conferencing systems, and audio recording systems. The audio device 104 may include a processor 202, a memory 204, the input device 108, an output device 206, and an audio processing system 208, which are all coupled to a bus 210. The bus 210 provides communication between the components of the audio device 104. The audio device 104 may comprise further components that are necessary for certain operations, but that are not necessarily used with respect to embodiments of the present invention. The audio processing system 208 is discussed in further detail in connection with FIG. 3.

The processor 202 is configured to execute instructions. The instructions are operational when executed by the processor 202 to direct the processor 202 to operate in accordance with the invention. The components and functions described herein may be comprised of instructions that are stored in the memory 204 or any other computer-readable storage medium. These instructions may be retrieved and executed by the processor 202. Some examples of instructions include software, program code, and firmware.

The memory 204 may permanently and/or temporarily store data, including various instructions. Some examples of the memory 204 are RAM and ROM. According to various embodiments, the memory 204 may comprise a computer readable storage medium, for example, memory devices, hard drives, disks, integrated circuits, and flash memory. Those skilled in the art are familiar with various instructions, processors, and memories.

According to various embodiments, the input device 108 may comprise any device that can convert an acoustic signal into an electric signal, for example, an acoustic-to-electric transducer (e.g., a microphone). In some embodiments, the microphone may be omnidirectional or unidirectional. The electric signal may be converted by an analog-to-digital converter (not shown) into a digital signal for processing or storage, in accordance with some embodiments. In other exemplary embodiments, the input device 108 may receive an already digitized signal, such as the decoded downlink signal.

In one exemplary embodiment, the output device 206 may comprise any device that provides an audio output to a user. The output device 206 may comprise, for example, a speaker on a conferencing device or an earpiece of a headset or handset. In another exemplary embodiment, the output device 206 may output a digitized signal which may be encoded and transmitted over a communications channel (e.g., an uplink signal).

FIG. 2B is a block diagram of a usage scenario of various exemplary embodiments of the present invention. The audio processing system 208A receives a downlink signal 212, which is enhanced and outputted by the output device 206. The signal from the speech source 102 in received by the input device 108 and enhanced by the audio processing system 208B. In turn, the enhanced signal from the speech source 102 may be transmitted as an uplink signal 214. In one embodiment, the audio processing system 208A and the audio processing system 208B are in communication such that acoustic echo cancellation or suppression may be performed on the signal from the input device 108. In another embodiment, the signal sent to the output device 206 may be modified based on the signal coming from the input device, for example, to increase audibility of the downlink signal 212 above the noise level in the environment 100 of the speech source 102. As one skilled in the art will appreciate, the audio processing system 208A and the audio processing system 208B may be combined into one system that facilitates two-way communication.

FIG. 3 is a block diagram of an exemplary embodiment of the audio processing system 208. The audio processing system 208 may comprise a frequency analysis module 302, a feature extractor module 304, an inference engine 306, a mask generator engine 308, a masking module 310, and a frequency synthesis module 312. In exemplary embodiments, the audio processing system 208 is embodied within a memory device, such as the memory 204. Although FIG. 3 describes the audio processing system 208 as including various engines and modules, fewer or more engines and modules may comprise the audio processing system 208 and/or any of the various engines and modules comprising the audio processing system 208 and still fall within the scope of various embodiments.

According to exemplary embodiments, the frequency analysis module 302 receives the monaural audio signal, for example, from the input device 108. As mentioned herein, the monaural audio signal received by the frequency analysis module 302 may comprise a decoded downlink signal. In one embodiment, the frequency analysis module 302 may be a filter bank. The frequency analysis module 302 may convert the monaural audio signal from the time-domain to the frequency-domain, such that the monaural audio signal may be separated into frequency sub-bands (also simply referred to as sub-bands).

The output of the frequency analysis module 302 may be sub-band signals that correspond to the sub-bands. According to some embodiments, the sub-band signals may result from performing a filtering operation on the monaural audio signal, where the bandwidth of the filter is narrower than the bandwidth of the monaural audio signal received by the frequency analysis module 302. Additionally, or alternatively, other filters such as short-time Fourier transform (SIFT), sub-band filter banks, modulated complex lapped transforms, cochlear models, wavelets, etc., may be used in conjunction with the frequency analysis module 302. Because most acoustic signals are complex and comprise more than one frequency, a sub-band analysis on the monaural audio signal received by the frequency analysis module 302 may be useful to determine certain characteristics of the signal within various frequency ranges during a frame (i.e., a predetermined period of time). In one embodiment, the frame is 5 milliseconds long.

The exemplary feature extractor module 304 is configured to receive the sub-band signals from the frequency analysis module 302 and determine one or more features (also referred to as cues). The cues may be used in discriminating between signals from the speech source 102 and the noise source 106. One such cue is a sub-band energy level. According to exemplary embodiments, the sub-band energy level may be an energy estimate of each of the sub-band signals over a time interval (i.e., frame) expressed in decibels. One definition of the sub-band energy level in sub-band k and frame n, Ek[n], may be a mean of a magnitude-squared sub-band signal. In exemplary embodiments, speech and noise level distributions may be estimated based on the sub-band energy levels.

In addition to the sub-band energy level, the feature extractor module 304 may calculate a wide-bandwidth sub-band energy level, in exemplary embodiments. The wide-bandwidth sub-band energy level may be an energy estimate within a frequency sub-band that has a larger bandwidth than that of individual sub-band signals outputted from the frequency analysis module 302. In exemplary embodiments, the wide-bandwidth sub-band energy level may be calculated by a weighted sum of neighboring sub-band energy levels around a center frequency sub-band. In one example, energy within an octave bandwidth may be obtained by summing, with equal weight, all sub-band energy levels within half an octave above and below the center frequency sub-band k as follows:

E ~ k [ n ] = m = 1 nT E m [ n ] · w k - m m = 1 nT w k - m , ( 1 )
where nT is a number of sub-bands, Wk=1 for |k|≦Koct/2 and zero otherwise, and Koct is a number of sub-bands per octave determined in the frequency analysis module 302. In exemplary embodiments, it may be inferred whether a given sub-band is dominated by speech or noise based on the energy level of a given sub-band and the energy level of surrounding sub-bands, for example, since speech activity may tend to produce correlated energy level changes across adjacent sub-bands.

The exemplary inference engine 306 is configured to determine a noise estimate for the audio signal. The noise estimate may comprise a stationary noise estimate, a non-stationary noise estimate, or various combinations thereof. In exemplary embodiments, the inference engine 306 may receive the sub-band energy levels, Ek[n], and the wide-bandwidth sub-band energy levels, {tilde over (E)}k[n], from the feature extractor module 304. The sub-band energy level and wide-bandwidth sub-band energy level may be combined to form a hypothesis of an instantaneous SNR (i.e., the SNR within a particular time frame) within a given sub-band using probability distributions of the wide-bandwidth sub-band energy levels over a given duration of history, in accordance with various embodiments.

In one example, the inference of the instantaneous SNR per sub-band may be obtained in two successive stages. Firstly, an initial prediction of the instantaneous SNR is made given two determined energy levels (e.g., the sub-band energy level and the wide-bandwidth sub-band energy level), which may be referred to as an observed instantaneous SNR. Secondly, adaptive smoothing of the observed instantaneous SNR may be performed, for example, using a Kalman filter. The inference engine 306 may finally generate a final noise estimate, which may be outputted to the mask generator engine 308. The inference engine 306 is discussed in further detail in connection with FIG. 4.

The exemplary mask generator engine 308 is configured to generate a mask which is applied by the masking module 310 to the audio signal. Generation of the mask may comprise generating and applying a preliminary mask to the audio signal, estimating a pitch of a resulting signal, and then, according to exemplary embodiments, modifying the mask based upon a pitch estimate, referred to as harmonic sharpening. The mask generator engine 308 is discussed in further detail in connection with FIG. 5.

In exemplary embodiments, the masking module 310 is configured to apply the mask to the sub-band signals of the audio signal. In some embodiments, the masking Module 310 up-samples the mask output of the mask generator engine 308. In some examples, a constrained linear interpolator may be used to interpolate mask values between two frames, which may limit a mask slew rate. In turn, the masking module 310 applies the mask, M(s, k), to the sub-band signals, C(s, k), from the frequency analysis module 302 such that
S enh(s,k)=C(s,kM(s,k),  (2)
where s and k are sample and sub-band indexes, respectively, and Senh(s, k) is an enhanced sub-band signal.

According to various embodiments, the frequency synthesis module 312 combines the enhanced sub-band signals to generate an enhanced, noise suppressed audio signal. In exemplary embodiments, the frequency synthesis module 312 converts the enhanced sub-band signals to the time-domain. The frequency synthesis module 312 may utilize an inverse of filtering operation(s) or transform performed by the frequency analysis module 302 to construct the enhanced audio signal.

Referring to FIG. 4, a block diagram of the exemplary inference engine 306 is shown in more detail. As depicted, the inference engine 306 comprises a stationary noise module 402, a non-stationary noise module 404, and a final noise module 406. The inference engine 306 is configured to generate a noise estimate, based at least in part, on stationarity (i.e., stationary and/or non-stationary).

The stationary noise module 402 may provide a stationary noise estimate based on an assumption that the noise levels change at slower rates than the speech levels. It is noted that in some embodiments, the stationary noise module 402 may comprise, or be referred to as, a minimum statistics tracker. The minimum statistics tracker is described in U.S. patent application Ser. No. 12/072,931, filed Feb. 29, 2008 and entitled “System and Method for Providing Single Microphone Noise Suppression Fallback,” which has been incorporated herein by reference. In exemplary embodiments, the stationary noise estimate may be obtained using the sub-band energy level and wide-bandwidth sub-band energy level provided by the feature extractor module 304 during speech pauses to extrapolate across regions where speech is present. One skilled in the art will recognize that various embodiments of the present invention do not need to rely on minimum statistics tracking to obtain a stationary noise estimate.

The non-stationary noise module 404 may infer the instantaneous SNR per time frame, as described herein, given a corresponding sub-band energy level and wide-bandwidth sub-band energy level provided by the feature extractor module 304. The non-stationary noise module 404 may perform several processes leading to a non-stationary noise estimate, in accordance with various embodiments.

One process that the non-stationary noise module 404 may perform includes calculating the wide-bandwidth sub-band energy level distribution of the mixed signal comprising both the wanted and the unwanted components (e.g., speech and noise). An energy level distribution for a given signal is a probability distribution as a function of energy level, which may, for example, indicate the likelihood of measuring a particular energy level in the signal at an arbitrary time. The energy level distribution may be derived from a histogram of measured energy levels over time, by an appropriate normalization. Calculating the wide-bandwidth sub-band energy level distribution of the mixed signal may be performed in an adaptive manner, for example, in order to adapt to long-term changes in the characteristics of the speech source 102 or noise source 106 (e.g., the speech source may change in volume or the noise type may change suddenly, such as from car noise to street noise). An adaptation time constant may be set for the energy level distribution and be converted to an equivalent number of frames, M. For a single sub-band, an energy level distribution, h(b), where b=1, . . . , B is the energy level bin index, may be adapted over time by accumulating energy level measurements in a “leaky” manner. As such, the adaptation of the energy level distribution may be represented as

h ( b ) = h ( b ) + [ ( L b E ~ [ n ] < L b + 1 ) - h ( b ) ] · 1 m , ( 3 )
where Lb and Lb+1 are lower and upper bounds in decibels for the energy level bin b, respectively, and m is similar to a time constant for a first-order leaky integrator, for example. In order to achieve a rapid initial convergence of the energy level distribution, m may be set equal to a number of frames from a start of the audio processing until m reaches M. In exemplary embodiments, once m=M, m may remain constant (e.g., m=1, 2, . . . , M, M, . . . ). In some cases, an explicit normalization of the energy level distribution may not be necessary to interpret the energy level distribution as a probability distribution since if L1=−∞, LB+1=∞, and the energy level bins are distinct and monotonically increasing, then

b = 1 B h ( b ) = 1.

Another process performed by the non-stationary noise module 404 may include inferring wide-bandwidth sub-band energy level distributions of both the speech and the noise separately. In one embodiment, the speech energy level distribution may be determined, partly, by the wide-bandwidth energy level distribution of a training clean speech signal, and partly, by an overall gain of the speech component of the mixed signal (e.g., the monaural audio signal) relative to the training clean speech signal. The overall gain may be determined by maximizing a cross-correlation across energy level and sub-band indices between a wide-bandwidth energy level distribution of the training clean speech signal and a wide-bandwidth energy level distribution of the mixed signal.

In another embodiment, the overall gain may be determined, partly, by a median value across sub-bands of the difference between an upper percentile energy level (e.g., 95% level) of the mixed signal and an upper percentile energy level of the training speech. The median may be used rather than a mean to improve robustness when isolated bands are severely corrupted by noise. In exemplary embodiments, the upper percentile value may be adjusted using an external voice activity detector, based upon an average percentage of speech activity over the adaptation time constant of the energy level distribution of the mixed signal.

A noise energy level distribution may represent the wide-bandwidth sub-band energy level distribution of the noise, according to some embodiments. The noise energy level distribution may be inferred from the mixture energy level distribution. One solution that may be adequate for many noise sources assumes that noise dominates lower energy levels of the mixture distribution. By setting a percentile level between zero and one, which reflects a percentage of time that speech is expected to be absent, a noise energy level distribution, hn(b), may be obtained from the mixture distribution, hm(b), using, for example:

h n ( b ) = { h m ( b ) η · b = 1 B h m ( b ) ; b η · b = 1 B h m ( b ) 0 ; b > η · b = 1 B h m ( b ) . ( 4 )
In an alternative embodiment, the noise distribution may be obtained by subtracting the speech energy level distribution from the mixture distribution.

The non-stationary noise module 404 may also infer the instantaneous SNR by factoring in that a wide-bandwidth energy level measurement or “observation” of a sub-band of the mixed signal falling into an energy level bin b may correspond to one of two conditions:

    • (i) speech is present at the energy level bin b and noise is present at any energy level bin<b, or
    • (ii) noise is present at the energy level bin b and speech is present at any energy level bin<b.
      The observed instantaneous SNR, ISNR(b), may be determined as a function of wide-bandwidth energy level bin b, for example, as follows:

ISNR ( b ) = h s ( b ) · g s ( b ) + h n ( b ) · g n ( b ) h s ( b ) + h n ( b ) , ( 5 )
where gs(b) is an expectation of ISNR(b) in condition (i) and gn(b) is the expectation of ISNR(b) in condition (ii). The expectations of ISNR(b) may be represented, for example, by:

g s ( b ) = δ · p = 1 b - 1 ( b - p ) · h n ( p ) and ( 6 ) g n ( b ) = δ · p = 1 b - 1 ( b - p ) · h s ( p ) , ( 7 )
where δ is a resolution of the energy level bins. In one embodiment, δ is roughly 3 decibels.

An observed variance of the instantaneous SNR, VISNR(b), may be determined by the non-stationary noise module 404, for example, by:

VISNR ( b ) = h s ( b ) · f s ( b ) + h n ( b ) · f n ( b ) h s ( b ) + h n ( b ) , ( 8 )
where fs(b) is an expectation of VISNR(b) in condition (i) and fn(b) is an expectation of VISNR(b) in condition (ii). The expectations of VISNR(b) may be expressed, in exemplary embodiments, by:

f s ( b ) = p = 1 b - 1 ISNR ( b ) - δ · ( b - p ) 2 · h n ( p ) and ( 9 ) f n ( b ) = p = 1 b - 1 ISNR ( b ) - δ · ( p - b ) 2 · h s ( p ) . ( 10 )
The observed variance of the instantaneous SNR may indicate an expected variance of the observed instantaneous SNR, which may be used in a Kalman filter to determine the Kalman gain.

The observed instantaneous SNR, ISNR(b), which is derived from the distribution of the wide-bandwidth energy level, {tilde over (E)}[n], in the mixture, may be adjusted based on the sub-band energy level, E[n], determined by the feature extractor module 304. In one exemplary embodiment, ISNR(b) may be perturbed by an amount proportional to the spectral contrast, E[n]−{tilde over (E)}[n]. The observed variance of the instantaneous SNR, VISNR(b), may also be adjusted based upon the sub-band energy level, E[n].

A real instantaneous SNR per sub-band in the mixed signal may be modeled as the state of a Kalman filter. The transition of the state from frame-to-frame may be optimally smoothed under the assumption that the temporal dynamics of both the speech and noise energy levels, and consequently the instantaneous SNR, are constrained. The observed instantaneous SNR may be considered to be observations of the state which are corrupted by Gaussian “observation noise.” VISNR(b) may provide an estimate of the variance of the observation noise. The prediction variance of the Kalman filter, may determine the a priori probability of a particular state transition between frames. The non-stationary noise module 404 may apply the Kalman filter to obtain an optimally and temporally-smoothed version of the observed instantaneous SNR, balancing the variance of the observation noise the prediction variance of the Kalman filter, in the form of a Kalman gain.

Some embodiments may invoke a simple model for an evolution of the state x[n], such as the instantaneous SNR, from frame n−1 to n that assumes
x[n]=x[n−1]+w[n],  (11)
where w[n]˜N[0, Q] is a process noise with prediction variance Q. In some examples, Q may be assumed to be constant based on empirical data. In turn, the observed instantaneous SNR at frame n may be measured to be, for example:
z[n]=x[n]+v[n],  (12)
where v[n]˜N(0, R[n]) is an observation noise with variance R[n] (e.g., VISNR(b)).

Using this simple model, a prediction stage and an update stage of the Kalman filter may estimate the state x[n] given noisy observation z[n] (e.g., the observed instantaneous SNR at frame n) and the previous state x[n−1]. For example, the prediction stage may predict the state in the current frame n as
{circumflex over (x)}[n]=x[n−1].  (13)
This may be interpreted as an expectation that the instantaneous SNR may remain fairly constant on average from frame to frame. A predicted variance of the state estimate may follow as
{circumflex over (p)}[n]=p[n−1]+Q.  (14)
The update stage of the Kalman filter may comprise determining a measurement residual, the Kalman gain, an updated state estimate, and an updated variance of the state estimate. The measurement residual may be calculated, for example, as
y[n]=z[n]−{circumflex over (x)}[n].  (15)
In exemplary embodiments, the Kalman gain may be calculated as

G [ n ] = p ^ [ n ] p ^ [ n ] + R [ n ] . ( 16 )
In various embodiments, the updated state estimate and the updated variance of the state estimate may be determined by
x[n]=x[n]+G[n]·y[n]  (17)
and
p[n]=(1−G[n])·{circumflex over (p)}[n],  (18)
respectively. The extent to which the Kalman filter temporally smoothes the observed instantaneous SNR may be dependent on the input mixed signal and may also depend on a value set for the process variance Q.

The non-stationary noise module 404 may finally derive a non-stationary noise estimate, NNE, from the state of the Kalman filter, x[n], and the stationary noise estimate, SNE, determined in the stationary noise module 402, for example, such that

NNE = max ( SNE , E [ n ] 1 + 10 ( ISNR Kalman - Δ SNR ) / 10 ) , ( 19 )
where E[n] is a sub-band energy of the mixed signal and ΔSNR is an offset applied to x[n]. Typically, ISNR may be close or equal to zero, although in some examples, ΔSNR may be made dependent on the SNR determined for the mixture, for example, across all sub-bands.

The exemplary final noise module 406 is configured to combine the non-stationary noise estimate determined by the non-stationary noise module 404 with the stationary noise estimate determined by the stationary noise module 402 into a final noise estimate. Since the stationary noise estimate may be fairly insensitive to the SNR, and at low SNRs the non-stationary noise estimate may not be reliable, the final noise module 406 may adjust a weight given to the non-stationary noise estimate and the stationary noise estimate to optimize the final noise estimate. According to various embodiments, the final noise module 406 may provide a smooth transition in the final noise estimate from being dominated by the non-stationary noise estimate at high SNRs to being dominated by the stationary noise estimate at low SNRs.

In some embodiments, calculation of the SNR in the final noise module 406 may be based on the overall gain of the speech level determined by the interference engine 306, the energy level of a training speech signal, the noise estimate determined by the stationary noise module 402, or combinations thereof.

In some embodiments, a transition region in the SNR may be defined by a lower and an upper threshold in decibels as SNRlow and SNRhigh, respectively. A transition, or cross-fade, between the stationary noise estimate and the non-stationary noise estimate may be determined by linear interpolation in the logarithmic domain to obtain the final noise estimate as follows:
NE=w·NNE+(1−w)·SNE,  (20)
where NE is the final noise estimate and

w = min ( 1 , max ( 0 , SNR - SNR low SNR high - SNR low ) ) . ( 21 )

FIG. 5 is a block diagram of an exemplary embodiment of the mask generator engine 308. The mask generator engine 308 may comprise a mask module 502, a modifier/reconstructor module 504, a pitch estimation module 506, and a harmonic sharpening module 508, according to various embodiments.

The exemplary mask module 502 is configured to generate a preliminary mask and a perceptual mask based on the final noise estimate received from the inference engine 306. It is noted that in various embodiments, the mask module 502 may comprise, or be referred to as, an adaptive intelligent suppression (AIS) generator. The AIS generator is described in U.S. patent application Ser. No. 11/825,563, filed Jul. 6, 2007 and entitled “System and Method for Adaptive Intelligent Noise Suppression,” which has been incorporated herein by reference. In one embodiment, the mask module 502 may comprise a Wiener filter. In exemplary embodiments, the preliminary mask may be subsequently used by the modifier/reconstructor module 504 and the perceptual mask may be subsequently used by the harmonic sharpening module 508, as discussed further herein.

In exemplary embodiments, the modifier/reconstructor module 504 may generate a preliminary signal (also referred to as a preliminary noise suppressed signal). As such, the modifier/reconstructor module 504 may be referred to as a preliminary signal module in various embodiments. The preliminary signal, Sprelim(s, k), may be generated by applying the preliminary mask, Mprelim(s, k), to the sub-band signals, C(s, k), from the frequency analysis module 302 such that, for example,
S prelim(s,k)=C(s,kM prelim(s,k),  (22)
where s and k are sample and sub-band indexes, respectively. According to some embodiments, the modifier/reconstructor module 504 may convert the preliminary signal to the time-domain (e.g., using an inverse of the filtering operations performed by the frequency analysis module 302) prior to outputting the preliminary signal to the pitch estimation module 506. In exemplary embodiments, reducing the noise in the signal inputted to the pitch estimation module 506 relative to the monaural input signal, may improve pitch estimation performance. In this regard, the preliminary mask generated by the mask module 502 may be optimized for subsequent pitch estimation performance, whereas the perceptual mask generated by the mask module 502 may be optimized for overall subjective quality. In other embodiments, a pitch estimation method may be applied directly to the input monaural signal, and the preliminary mask and modifier/reconstructor module 504 may be unnecessary.

The pitch estimation module 506 is configured to determine a pitch estimate based on the preliminary signal from the modifier/reconstructor module 504. In embodiments where the preliminary signal has been converted to the time-domain, an estimate of pitch in each frame may be obtained using a time-domain autocorrelation based method. In one embodiment, the time-domain autocorrelation based method may be similar to an open-loop pitch analysis in ITU-T recommendation G.729. The estimate of the pitch may be used by the harmonic sharpening module 508 as discussed herein.

The exemplary harmonic sharpening module 508 is configured to improve the SNR of the output of the system. According to various embodiments, the harmonic sharpening module 508 may modify a perceptual mask generated by the mask module 502 such that additional gain may be applied to sub-bands containing speech harmonics. The additional gain may only be applied to sub-bands containing speech harmonics that are resolvable by the frequency analysis module 302, and/or when operating at low SNRs (e.g., SNR<10 decibels). Applying positive gain to sub-bands that have much wider bandwidth than the pitch estimate, may inadvertently boost any noise that also occupies that sub-band.

The harmonic sharpening module 508 may apply a gain in decibels to a sub-band, k, neighboring a predicted speech harmonic, m, by an amount Wm(k−v), where v may be the closest sub-band in frequency to the predicted harmonic. In one simple exemplary embodiment, an additional gain may be applied only to the closest sub-band to the predicted harmonic, i.e., the gain function Wm(k−v)=0 if k≠v. Alternative gain functions may be utilized, such as where both the nearest sub-band to the harmonic and the sub-bands adjacent to the nearest sub-band have a positive gain. A harmonic sharpening mask may be computed that is equal to one everywhere except at the harmonics, at which the harmonic sharpening mask is equal to Wm(k−v) after converting from decibels to linear. The harmonic sharpening mask may be smoothed temporally, for example, by using a leaky integrator with a time constant of approximately 10 milliseconds. In exemplary embodiments, the perceptual mask and the harmonic sharpening mask may be combined to obtain a final mask to be outputted to the masking module 310. In one embodiment, the perceptual mask and the harmonic sharpening mask may be multiplicatively combined.

FIG. 6 is a flowchart of an exemplary process 600 for providing noise suppression on an audio signal. According to various embodiments, the process 600 may be performed by the audio processing system 208, the audio device 104, or any combination thereof.

At step 602, an audio signal is received. The audio signal may comprise the monaural audio signal having the wanted component (e.g., speech) and the unwanted component (e.g., noise). In some embodiments, the wanted signal may be speech, for example, emanating from the speech source 102, as described in connection with FIG. 1. In other embodiments, the audio signal may comprise the decoded downlink signal. As discussed herein, the frequency analysis module 302 may receive the audio signal, for example, from the input device 108. Furthermore, the frequency analysis module 302 may be a filter-bank, in accordance with some embodiments. The frequency analysis module 302 may also convert the audio signal from the time-domain into the frequency-domain for further processing. In one example, the audio signal may be separated into frequency sub-bands.

At step 604, energy estimates of the signal are determined. According to various embodiments, the energy estimates may be determined by the feature extractor module 304. According to one embodiment, a speech probability distribution and a noise probability distribution are determined based on the energy estimates.

At step 606, at least one characteristic of the signal is inferred based on the energy estimates. The at least one characteristic may be inferred by the inference engine 306, in accordance with various embodiments. In exemplary embodiments, the at least one characteristic comprises a noise estimate and/or a signal-to-noise ratio. Various assumptions may be involved in inferring the at least one characteristic. For example, a distribution of speech energy in the signal may be assumed to be different from a distribution of noise energy in the signal in one embodiment. Additionally, speech energy in the signal may be assumed to vary more rapidly than noise energy in the signal in some embodiments.

As discussed herein, the inference engine 306 may comprise the stationary noise module 402, the non-stationary noise module 404, the final noise module 406, and/or any combination thereof. The final noise module 406 may combine the stationary noise estimate from the stationary noise module 402 with the non-stationary noise estimate from the non-stationary noise module 404 into a final noise estimate. In some embodiments, the final noise estimate may be weighted towards the stationary noise estimate or the non-stationary noise estimate based on the estimate of the SNR of the audio signal in the final noise module 406.

At step 608, a mask based, in part, on the at least one characteristic is generated. The mask generator engine 308 may generate the mask in some embodiments. In exemplary embodiments, generating the mask may include using a pitch estimate, such as that provided by the pitch estimation module 506.

At step 610, the mask is applied to the signal to produce an enhanced signal. In exemplary embodiments, the masking module 310 may apply the mask to the signal from the frequency analysis module 302.

At step 612, the enhanced signal is outputted. The enhanced signal may then be outputted, for example, by a speaker or as an uplink signal. According to various embodiments, the enhanced signal may be converted to a time-domain, such as by the frequency synthesis module 312.

The above-described components and functions can be comprised of instructions that are stored on a computer-readable storage medium. The instructions can be retrieved and executed by a processor (e.g., the processor 202). Some examples of instructions are software, program code, and firmware. Some examples of storage medium are memory devices, tape, disks, integrated circuits, and servers. The instructions are operational when executed by the processor to direct the processor to operate in accord with the invention. Those skilled in the art are familiar with instructions, processor(s), and storage medium.

The above description is illustrative and not restrictive. Many variations of the invention will become apparent to those of skill in the art upon review of this disclosure. The scope of the invention should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims along with their full scope of equivalents.

Claims (18)

What is claimed is:
1. A method for enhancing a signal, the method comprising:
receiving the signal, the receiving the signal comprising converting the signal to a frequency-domain such that the signal is separated into sub-bands;
determining energy estimates of the signal for each sub-band of the signal;
calculating an energy level distribution based on the energy estimates over time, the energy level distribution being a probability distribution as a function of energy level;
determining a noise energy estimate for each sub-band of the signal and a speech energy estimate for each sub-band of the signal based on the energy level distribution;
generating a mask for each sub-band of the signal based, in part, on the noise energy estimate for each sub-band of the signal and speech energy estimate for each sub-band of the signal;
applying the mask to the signal for each sub-band of the signal to produce an enhanced signal; and
outputting the enhanced signal.
2. The method of claim 1, wherein outputting the enhanced signal comprises converting the enhanced signal to a time-domain.
3. The method of claim 1, wherein a distribution of speech energy in the signal is different from a distribution of noise energy in the signal.
4. The method of claim 1, wherein speech energy in the signal varies more rapidly than noise energy in the signal.
5. A system for enhancing a signal, the system comprising:
an input device configured to receive the signal;
a frequency analysis module configured to convert the signal to a frequency-domain such that the signal is separated into sub-bands;
a features module configured to determine energy estimates of the signal for each sub-band of the signal;
an inference engine configured to determine an energy level distribution based on the energy estimates over time, the energy level distribution being a probability distribution as a function of energy level, and the inference engine being further configured to determine a noise energy estimate for each sub-band of the signal and a speech energy estimate for each sub-band of the signal based on the energy level distribution;
a mask generator engine configured to generate a mask for each sub-band of the signal based, in part, on the noise energy estimate for each sub-band of the signal and speech energy estimate for each sub-band of the signal;
a masking module configured to apply the mask for each sub-band of the signal to the signal to produce an enhanced signal; and
an output device configured to output the enhanced signal.
6. The system of claim 5, further comprising a frequency synthesis module configured to convert the enhanced signal to a time-domain.
7. The system of claim 5, wherein the inference engine is further configured to assume that a distribution of speech energy in the signal is different from a distribution of noise energy in the signal.
8. The system of claim 5, wherein the inference engine is further configured to assume that speech energy in the signal varies more rapidly than noise energy in the signal.
9. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor for performing a method for enhancing a signal, the method comprising:
receiving the signal, the receiving the signal comprising converting the signal to a frequency-domain such that the signal is separated into sub-bands;
determining energy estimates of the signal for each sub-band of the signal;
determining an energy level distribution based on the energy estimates over time, the energy level distribution being a probability distribution as a function of energy level;
determining a noise energy estimate for each sub-band of the signal and a speech energy estimate for each sub-band of the signal based on the energy level distribution;
generating a mask for each sub-band of the signal based, in part, on the noise energy estimate for each sub-band of the signal and speech energy estimate for each sub-band of the signal;
applying the mask to the signal for each sub-band of the signal to produce an enhanced signal; and
outputting the enhanced signal.
10. The non-transitory computer readable storage medium of claim 9, wherein outputting the enhanced signal comprises converting the enhanced signal to a time-domain.
11. The non-transitory computer readable storage medium of claim 9, wherein speech energy in the signal varies more rapidly than noise energy in the signal.
12. The method of claim 1, wherein applying the mask results in attenuating signal components with low energy, and preserving signal components with high energy.
13. The method of claim 1, wherein determining the energy level distribution comprises tracking one or more percentile estimates of the energy estimates over time.
14. The method of claim 1, wherein calculating the energy level distribution comprises tracking an energy histogram over time.
15. The method of claim 1, wherein determining the noise energy estimate for each sub-band of the signal and the speech energy estimate for each sub-band of the signal comprises determining a speech energy histogram and a noise energy histogram.
16. The method of claim 1, wherein determining the noise energy estimate for each sub-band of the signal and the speech energy estimate for each sub-band of the signal depends on a predetermined speech energy histogram.
17. The method of claim 1, wherein determining the noise energy estimate for each sub-band of the signal and the speech energy estimate for each sub-band of the signal is based on one or more differences between tracked percentile estimates.
18. The method of claim 1, wherein determining the noise energy estimate for each sub-band of the signal is performed by computing a non-linear function of at least one of the energy estimates for the particular sub-band, a temporally-smoothed energy estimate for the particular sub-band, and a frequency-smoothed energy estimate for the particular sub-band.
US12/217,076 2008-06-30 2008-06-30 System and method for enhancing a monaural audio signal Active 2030-10-20 US8521530B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US12/217,076 US8521530B1 (en) 2008-06-30 2008-06-30 System and method for enhancing a monaural audio signal

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US12/217,076 US8521530B1 (en) 2008-06-30 2008-06-30 System and method for enhancing a monaural audio signal

Publications (1)

Publication Number Publication Date
US8521530B1 true US8521530B1 (en) 2013-08-27

Family

ID=48999836

Family Applications (1)

Application Number Title Priority Date Filing Date
US12/217,076 Active 2030-10-20 US8521530B1 (en) 2008-06-30 2008-06-30 System and method for enhancing a monaural audio signal

Country Status (1)

Country Link
US (1) US8521530B1 (en)

Cited By (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110142256A1 (en) * 2009-12-16 2011-06-16 Samsung Electronics Co., Ltd. Method and apparatus for removing noise from input signal in noisy environment
US20110191101A1 (en) * 2008-08-05 2011-08-04 Christian Uhle Apparatus and Method for Processing an Audio Signal for Speech Enhancement Using a Feature Extraction
US20120209601A1 (en) * 2011-01-10 2012-08-16 Aliphcom Dynamic enhancement of audio (DAE) in headset systems
US20130260692A1 (en) * 2012-03-29 2013-10-03 Bose Corporation Automobile communication system
US20130332157A1 (en) * 2012-06-08 2013-12-12 Apple Inc. Audio noise estimation and audio noise reduction using multiple microphones
US20140129215A1 (en) * 2012-11-02 2014-05-08 Samsung Electronics Co., Ltd. Electronic device and method for estimating quality of speech signal
EP2858382A1 (en) * 2013-10-01 2015-04-08 Starkey Laboratories, Inc. System and method for selective harmonic enhancement for hearing assistance devices
US20150127335A1 (en) * 2013-11-07 2015-05-07 Nvidia Corporation Voice trigger
US9185500B2 (en) 2008-06-02 2015-11-10 Starkey Laboratories, Inc. Compression of spaced sources for hearing assistance devices
WO2015189261A1 (en) * 2014-06-13 2015-12-17 Retune DSP ApS Multi-band noise reduction system and methodology for digital audio signals
WO2016063795A1 (en) * 2014-10-21 2016-04-28 Mitsubishi Electric Corporation Method for transforming a noisy speech signal to an enhanced speech signal
US9332360B2 (en) 2008-06-02 2016-05-03 Starkey Laboratories, Inc. Compression and mixing for hearing assistance devices
US9467779B2 (en) 2014-05-13 2016-10-11 Apple Inc. Microphone partial occlusion detector
US9524735B2 (en) 2014-01-31 2016-12-20 Apple Inc. Threshold adaptation in two-channel noise estimation and voice activity detection
US9536540B2 (en) 2013-07-19 2017-01-03 Knowles Electronics, Llc Speech signal separation and synthesis based on auditory scene analysis and speech modeling
US9640194B1 (en) 2012-10-04 2017-05-02 Knowles Electronics, Llc Noise suppression for speech processing based on machine-learning mask estimation
US9699554B1 (en) 2010-04-21 2017-07-04 Knowles Electronics, Llc Adaptive signal equalization
US9769550B2 (en) 2013-11-06 2017-09-19 Nvidia Corporation Efficient digital microphone receiver process and system
US9799330B2 (en) 2014-08-28 2017-10-24 Knowles Electronics, Llc Multi-sourced noise suppression
US20170325020A1 (en) * 2014-12-12 2017-11-09 Nuance Communications, Inc. System and method for generating a self-steering beamformer
US9830899B1 (en) 2006-05-25 2017-11-28 Knowles Electronics, Llc Adaptive noise cancellation
US9924283B2 (en) 2008-06-02 2018-03-20 Starkey Laboratories, Inc. Enhanced dynamics processing of streaming audio by source separation and remixing
US10269368B2 (en) 2014-06-13 2019-04-23 Oticon A/S Audio processing device and a method for estimating a signal-to-noise-ratio of a sound signal
US10412490B2 (en) 2016-02-25 2019-09-10 Dolby Laboratories Licensing Corporation Multitalker optimised beamforming system and method
US10433076B2 (en) 2016-05-30 2019-10-01 Oticon A/S Audio processing device and a method for estimating a signal-to-noise-ratio of a sound signal

Citations (258)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3976863A (en) 1974-07-01 1976-08-24 Alfred Engel Optimal decoder for non-stationary signals
US3978287A (en) 1974-12-11 1976-08-31 Nasa Real time analysis of voiced sounds
US4137510A (en) 1976-01-22 1979-01-30 Victor Company Of Japan, Ltd. Frequency band dividing filter
US4433604A (en) 1981-09-22 1984-02-28 Texas Instruments Incorporated Frequency domain digital encoding technique for musical signals
US4516259A (en) 1981-05-11 1985-05-07 Kokusai Denshin Denwa Co., Ltd. Speech analysis-synthesis system
US4535473A (en) * 1981-10-31 1985-08-13 Tokyo Shibaura Denki Kabushiki Kaisha Apparatus for detecting the duration of voice
US4536844A (en) 1983-04-26 1985-08-20 Fairchild Camera And Instrument Corporation Method and apparatus for simulating aural response information
US4581758A (en) 1983-11-04 1986-04-08 At&T Bell Laboratories Acoustic direction identification system
US4628529A (en) 1985-07-01 1986-12-09 Motorola, Inc. Noise suppression system
US4630304A (en) 1985-07-01 1986-12-16 Motorola, Inc. Automatic background noise estimator for a noise suppression system
US4649505A (en) 1984-07-02 1987-03-10 General Electric Company Two-input crosstalk-resistant adaptive noise canceller
US4658426A (en) 1985-10-10 1987-04-14 Harold Antin Adaptive noise suppressor
US4674125A (en) 1983-06-27 1987-06-16 Rca Corporation Real-time hierarchal pyramid signal processing apparatus
US4718104A (en) 1984-11-27 1988-01-05 Rca Corporation Filter-subtract-decimate hierarchical pyramid signal analyzing and synthesizing technique
US4811404A (en) 1987-10-01 1989-03-07 Motorola, Inc. Noise suppression system
US4812996A (en) 1986-11-26 1989-03-14 Tektronix, Inc. Signal viewing instrumentation control system
US4864620A (en) 1987-12-21 1989-09-05 The Dsp Group, Inc. Method for performing time-scale modification of speech information or speech signals
US4920508A (en) 1986-05-22 1990-04-24 Inmos Limited Multistage digital signal multiplication and addition
US5027410A (en) 1988-11-10 1991-06-25 Wisconsin Alumni Research Foundation Adaptive, programmable signal processing and filtering for hearing aids
US5054085A (en) 1983-05-18 1991-10-01 Speech Systems, Inc. Preprocessing system for speech recognition
US5058419A (en) 1990-04-10 1991-10-22 Earl H. Ruble Method and apparatus for determining the location of a sound source
US5099738A (en) 1989-01-03 1992-03-31 Hotz Instruments Technology, Inc. MIDI musical translator
US5119711A (en) 1990-11-01 1992-06-09 International Business Machines Corporation Midi file translation
US5142961A (en) 1989-11-07 1992-09-01 Fred Paroutaud Method and apparatus for stimulation of acoustic musical instruments
US5150413A (en) 1984-03-23 1992-09-22 Ricoh Company, Ltd. Extraction of phonemic information
US5175769A (en) 1991-07-23 1992-12-29 Rolm Systems Method for time-scale modification of signals
US5187776A (en) 1989-06-16 1993-02-16 International Business Machines Corp. Image editor zoom function
US5208864A (en) 1989-03-10 1993-05-04 Nippon Telegraph & Telephone Corporation Method of detecting acoustic signal
US5210366A (en) 1991-06-10 1993-05-11 Sykes Jr Richard O Method and device for detecting and separating voices in a complex musical composition
US5224170A (en) 1991-04-15 1993-06-29 Hewlett-Packard Company Time domain compensation for transducer mismatch
US5230022A (en) 1990-06-22 1993-07-20 Clarion Co., Ltd. Low frequency compensating circuit for audio signals
US5289273A (en) 1989-09-20 1994-02-22 Semborg-Recrob, Corp. Animated character system with real-time control
US5319736A (en) 1989-12-06 1994-06-07 National Research Council Of Canada System for separating speech from background noise
US5323459A (en) 1992-11-10 1994-06-21 Nec Corporation Multi-channel echo canceler
US5341432A (en) 1989-10-06 1994-08-23 Matsushita Electric Industrial Co., Ltd. Apparatus and method for performing speech rate modification and improved fidelity
US5381473A (en) 1992-10-29 1995-01-10 Andrea Electronics Corporation Noise cancellation apparatus
US5381512A (en) 1992-06-24 1995-01-10 Moscom Corporation Method and apparatus for speech feature recognition based on models of auditory signal processing
US5400409A (en) 1992-12-23 1995-03-21 Daimler-Benz Ag Noise-reduction method for noise-affected voice channels
US5402493A (en) 1992-11-02 1995-03-28 Central Institute For The Deaf Electronic simulator of non-linear and active cochlear spectrum analysis
US5402496A (en) 1992-07-13 1995-03-28 Minnesota Mining And Manufacturing Company Auditory prosthesis, noise suppression apparatus and feedback suppression apparatus having focused adaptive filtering
US5471195A (en) 1994-05-16 1995-11-28 C & K Systems, Inc. Direction-sensing acoustic glass break detecting system
US5473702A (en) 1992-06-03 1995-12-05 Oki Electric Industry Co., Ltd. Adaptive noise canceller
US5473759A (en) 1993-02-22 1995-12-05 Apple Computer, Inc. Sound analysis and resynthesis using correlograms
US5479564A (en) 1991-08-09 1995-12-26 U.S. Philips Corporation Method and apparatus for manipulating pitch and/or duration of a signal
US5502663A (en) 1992-12-14 1996-03-26 Apple Computer, Inc. Digital filter having independent damping and frequency parameters
US5544250A (en) 1994-07-18 1996-08-06 Motorola Noise suppression system and method therefor
US5574824A (en) 1994-04-11 1996-11-12 The United States Of America As Represented By The Secretary Of The Air Force Analysis/synthesis-based microphone array speech enhancer with variable signal distortion
US5583784A (en) 1993-05-14 1996-12-10 Fraunhofer-Gesellschaft Zur Forderung Der Angewandten Forschung E.V. Frequency analysis method
US5587998A (en) 1995-03-03 1996-12-24 At&T Method and apparatus for reducing residual far-end echo in voice communication networks
US5590241A (en) 1993-04-30 1996-12-31 Motorola Inc. Speech processing system and method for enhancing a speech signal in a noisy environment
US5602962A (en) 1993-09-07 1997-02-11 U.S. Philips Corporation Mobile radio set comprising a speech processing arrangement
US5675778A (en) 1993-10-04 1997-10-07 Fostex Corporation Of America Method and apparatus for audio editing incorporating visual comparison
US5682463A (en) 1995-02-06 1997-10-28 Lucent Technologies Inc. Perceptual audio compression based on loudness uncertainty
US5694474A (en) 1995-09-18 1997-12-02 Interval Research Corporation Adaptive filter for signal processing and method therefor
US5706395A (en) 1995-04-19 1998-01-06 Texas Instruments Incorporated Adaptive weiner filtering using a dynamic suppression factor
US5717829A (en) 1994-07-28 1998-02-10 Sony Corporation Pitch control of memory addressing for changing speed of audio playback
US5729612A (en) 1994-08-05 1998-03-17 Aureal Semiconductor Inc. Method and apparatus for measuring head-related transfer functions
US5732189A (en) 1995-12-22 1998-03-24 Lucent Technologies Inc. Audio signal coding with a signal adaptive filterbank
US5749064A (en) 1996-03-01 1998-05-05 Texas Instruments Incorporated Method and system for time scale modification utilizing feature vectors about zero crossing points
US5757937A (en) 1996-01-31 1998-05-26 Nippon Telegraph And Telephone Corporation Acoustic noise suppressor
US5792971A (en) 1995-09-29 1998-08-11 Opcode Systems, Inc. Method and system for editing digital audio information with music-like parameters
US5796819A (en) 1996-07-24 1998-08-18 Ericsson Inc. Echo canceller for non-linear circuits
US5806025A (en) 1996-08-07 1998-09-08 U S West, Inc. Method and system for adaptive filtering of speech signals using signal-to-noise ratio to choose subband filter bank
US5809463A (en) 1995-09-15 1998-09-15 Hughes Electronics Method of detecting double talk in an echo canceller
US5825320A (en) 1996-03-19 1998-10-20 Sony Corporation Gain control method for audio encoding device
US5839101A (en) 1995-12-12 1998-11-17 Nokia Mobile Phones Ltd. Noise suppressor and method for suppressing background noise in noisy speech, and a mobile station
US5845243A (en) * 1995-10-13 1998-12-01 U.S. Robotics Mobile Communications Corp. Method and apparatus for wavelet based data compression having adaptive bit rate control for compression of audio information
US5920840A (en) 1995-02-28 1999-07-06 Motorola, Inc. Communication system and method using a speaker dependent time-scaling technique
US5933495A (en) 1997-02-07 1999-08-03 Texas Instruments Incorporated Subband acoustic noise suppression
US5943429A (en) 1995-01-30 1999-08-24 Telefonaktiebolaget Lm Ericsson Spectral subtraction noise suppression method
US5956674A (en) 1995-12-01 1999-09-21 Digital Theater Systems, Inc. Multi-channel predictive subband audio coder using psychoacoustic adaptive bit allocation in frequency, time and over the multiple channels
US5978824A (en) 1997-01-29 1999-11-02 Nec Corporation Noise canceler
US5983139A (en) 1997-05-01 1999-11-09 Med-El Elektromedizinische Gerate Ges.M.B.H. Cochlear implant system
US5990405A (en) 1998-07-08 1999-11-23 Gibson Guitar Corp. System and method for generating and controlling a simulated musical concert experience
US6002776A (en) 1995-09-18 1999-12-14 Interval Research Corporation Directional acoustic signal processor and method therefor
US6061456A (en) 1992-10-29 2000-05-09 Andrea Electronics Corporation Noise cancellation apparatus
US6072881A (en) 1996-07-08 2000-06-06 Chiefs Voice Incorporated Microphone noise rejection system
US6097820A (en) 1996-12-23 2000-08-01 Lucent Technologies Inc. System and method for suppressing noise in digitally represented voice signals
US6108626A (en) 1995-10-27 2000-08-22 Cselt-Centro Studi E Laboratori Telecomunicazioni S.P.A. Object oriented audio coding
US6122610A (en) 1998-09-23 2000-09-19 Verance Corporation Noise suppression for low bitrate speech coder
US6134524A (en) * 1997-10-24 2000-10-17 Nortel Networks Corporation Method and apparatus to detect and delimit foreground speech
US6137349A (en) 1997-07-02 2000-10-24 Micronas Intermetall Gmbh Filter combination for sampling rate conversion
US6140809A (en) 1996-08-09 2000-10-31 Advantest Corporation Spectrum analyzer
US6173255B1 (en) 1998-08-18 2001-01-09 Lockheed Martin Corporation Synchronized overlap add voice processing using windows and one bit correlators
US6180273B1 (en) 1995-08-30 2001-01-30 Honda Giken Kogyo Kabushiki Kaisha Fuel cell with cooling medium circulation arrangement and method
US6205422B1 (en) * 1998-11-30 2001-03-20 Microsoft Corporation Morphological pure speech detection using valley percentage
US6216103B1 (en) 1997-10-20 2001-04-10 Sony Corporation Method for implementing a speech recognition system to determine speech endpoints during conditions with background noise
US6219408B1 (en) 1999-05-28 2001-04-17 Paul Kurth Apparatus and method for simultaneously transmitting biomedical data and human voice over conventional telephone lines
US6222927B1 (en) 1996-06-19 2001-04-24 The University Of Illinois Binaural signal processing system and method
US6223090B1 (en) 1998-08-24 2001-04-24 The United States Of America As Represented By The Secretary Of The Air Force Manikin positioning for acoustic measuring
US6226616B1 (en) 1999-06-21 2001-05-01 Digital Theater Systems, Inc. Sound quality of established low bit-rate audio coding systems without loss of decoder compatibility
US6263307B1 (en) 1995-04-19 2001-07-17 Texas Instruments Incorporated Adaptive weiner filtering using line spectral frequencies
US6266633B1 (en) 1998-12-22 2001-07-24 Itt Manufacturing Enterprises Noise suppression and channel equalization preprocessor for speech and speaker recognizers: method and apparatus
US20010016020A1 (en) 1999-04-12 2001-08-23 Harald Gustafsson System and method for dual microphone signal noise reduction using spectral subtraction
US20010031053A1 (en) 1996-06-19 2001-10-18 Feng Albert S. Binaural signal processing techniques
US20010038699A1 (en) 2000-03-20 2001-11-08 Audia Technology, Inc. Automatic directional processing control for multi-microphone system
US6317501B1 (en) 1997-06-26 2001-11-13 Fujitsu Limited Microphone array apparatus
US6321193B1 (en) * 1998-01-27 2001-11-20 Telefonaktiebolaget Lm Ericsson Distance and distortion estimation method and apparatus in channel optimized vector quantization
US20020002455A1 (en) 1998-01-09 2002-01-03 At&T Corporation Core estimator and adaptive gains from signal to noise ratio in a hybrid speech enhancement system
US6339758B1 (en) 1998-07-31 2002-01-15 Kabushiki Kaisha Toshiba Noise suppress processing apparatus and method
US20020009203A1 (en) 2000-03-31 2002-01-24 Gamze Erten Method and apparatus for voice signal extraction
US6343267B1 (en) 1998-04-30 2002-01-29 Matsushita Electric Industrial Co., Ltd. Dimensionality reduction for speaker normalization and speaker and environment adaptation using eigenvoice techniques
US6355869B1 (en) 1999-08-19 2002-03-12 Duane Mitton Method and system for creating musical scores from musical recordings
US6363345B1 (en) 1999-02-18 2002-03-26 Andrea Electronics Corporation System, method and apparatus for cancelling noise
US6381570B2 (en) 1999-02-12 2002-04-30 Telogy Networks, Inc. Adaptive two-threshold method for discriminating noise from speech in a communication signal
US6430295B1 (en) 1997-07-11 2002-08-06 Telefonaktiebolaget Lm Ericsson (Publ) Methods and apparatus for measuring signal level and delay at multiple sensors
US6434417B1 (en) 2000-03-28 2002-08-13 Cardiac Pacemakers, Inc. Method and system for detecting cardiac depolarization
US20020116187A1 (en) 2000-10-04 2002-08-22 Gamze Erten Speech detection
US6449586B1 (en) 1997-08-01 2002-09-10 Nec Corporation Control method of adaptive array and adaptive array apparatus
US6453289B1 (en) * 1998-07-24 2002-09-17 Hughes Electronics Corporation Method of noise reduction for speech codecs
US20020133334A1 (en) 2001-02-02 2002-09-19 Geert Coorman Time scale modification of digitally sampled waveforms in the time domain
US20020147595A1 (en) 2001-02-22 2002-10-10 Frank Baumgarte Cochlear filter bank structure for determining masked thresholds for use in perceptual audio coding
US6469732B1 (en) 1998-11-06 2002-10-22 Vtel Corporation Acoustic source location using a microphone array
US6487257B1 (en) 1999-04-12 2002-11-26 Telefonaktiebolaget L M Ericsson Signal noise reduction by time-domain spectral subtraction using fixed filters
US20020184013A1 (en) 2001-04-20 2002-12-05 Alcatel Method of masking noise modulation and disturbing noise in voice communication
US6496795B1 (en) 1999-05-05 2002-12-17 Microsoft Corporation Modulated complex lapped transform for integrated signal enhancement and coding
US20030014248A1 (en) 2001-04-27 2003-01-16 Csem, Centre Suisse D'electronique Et De Microtechnique Sa Method and system for enhancing speech in a noisy environment
US6513004B1 (en) 1999-11-24 2003-01-28 Matsushita Electric Industrial Co., Ltd. Optimized local feature extraction for automatic speech recognition
US6516066B2 (en) 2000-04-11 2003-02-04 Nec Corporation Apparatus for detecting direction of sound source and turning microphone toward sound source
US20030026437A1 (en) 2001-07-20 2003-02-06 Janse Cornelis Pieter Sound reinforcement system having an multi microphone echo suppressor as post processor
US20030033140A1 (en) 2001-04-05 2003-02-13 Rakesh Taori Time-scale modification of signals
US20030039369A1 (en) 2001-07-04 2003-02-27 Bullen Robert Bruce Environmental noise monitoring
US20030040908A1 (en) 2001-02-12 2003-02-27 Fortemedia, Inc. Noise suppression for speech signal in an automobile
US6529606B1 (en) 1997-05-16 2003-03-04 Motorola, Inc. Method and system for reducing undesired signals in a communication environment
US20030061032A1 (en) 2001-09-24 2003-03-27 Clarity, Llc Selective sound enhancement
US20030063759A1 (en) 2001-08-08 2003-04-03 Brennan Robert L. Directional audio signal processing using an oversampled filterbank
US6549630B1 (en) 2000-02-04 2003-04-15 Plantronics, Inc. Signal expander with discrimination between close and distant acoustic source
US20030072460A1 (en) 2001-07-17 2003-04-17 Clarity Llc Directional sound acquisition
US20030072382A1 (en) 1996-08-29 2003-04-17 Cisco Systems, Inc. Spatio-temporal processing for communication
US20030095667A1 (en) 2001-11-14 2003-05-22 Applied Neurosystems Corporation Computation of multi-sensor time delays
US20030101048A1 (en) 2001-10-30 2003-05-29 Chunghwa Telecom Co., Ltd. Suppression system of background noise of voice sounds signals and the method thereof
US20030099345A1 (en) 2001-11-27 2003-05-29 Siemens Information Telephone having improved hands free operation audio quality and method of operation thereof
US20030103632A1 (en) 2001-12-03 2003-06-05 Rafik Goubran Adaptive sound masking system and method
US6584203B2 (en) 2001-07-18 2003-06-24 Agere Systems Inc. Second-order adaptive differential microphone array
US20030128851A1 (en) 2001-06-06 2003-07-10 Satoru Furuta Noise suppressor
US20030138116A1 (en) 2000-05-10 2003-07-24 Jones Douglas L. Interference suppression techniques
US20030147538A1 (en) 2002-02-05 2003-08-07 Mh Acoustics, Llc, A Delaware Corporation Reducing noise in audio systems
US20030169891A1 (en) 2002-03-08 2003-09-11 Ryan Jim G. Low-noise directional microphone system
US6622030B1 (en) 2000-06-29 2003-09-16 Ericsson Inc. Echo suppression using adaptive gain based on residual echo energy
US20030228023A1 (en) 2002-03-27 2003-12-11 Burnett Gregory C. Microphone and Voice Activity Detection (VAD) configurations for use with communication systems
US20040013276A1 (en) 2002-03-22 2004-01-22 Ellis Richard Thompson Analog audio signal enhancement system using a noise suppression algorithm
JP2004053895A (en) 2002-07-19 2004-02-19 Matsushita Electric Ind Co Ltd Device and method for audio decoding, and program
US20040047464A1 (en) 2002-09-11 2004-03-11 Zhuliang Yu Adaptive noise cancelling microphone system
US20040057574A1 (en) 2002-09-20 2004-03-25 Christof Faller Suppression of echo signals and the like
US6717991B1 (en) 1998-05-27 2004-04-06 Telefonaktiebolaget Lm Ericsson (Publ) System and method for dual microphone signal noise reduction using spectral subtraction
US6718309B1 (en) 2000-07-26 2004-04-06 Ssi Corporation Continuously variable time scale modification of digital audio signals
US20040078199A1 (en) 2002-08-20 2004-04-22 Hanoh Kremer Method for auditory based noise reduction and an apparatus for auditory based noise reduction
US6738482B1 (en) 1999-09-27 2004-05-18 Jaber Associates, Llc Noise suppression system with dual microphone echo cancellation
US6748095B1 (en) 1998-06-23 2004-06-08 Worldcom, Inc. Headset with multiple connections
US20040131178A1 (en) 2001-05-14 2004-07-08 Mark Shahaf Telephone apparatus and a communication method using such apparatus
US20040133421A1 (en) 2000-07-19 2004-07-08 Burnett Gregory C. Voice activity detector (VAD) -based multiple-microphone acoustic noise suppression
US20040165736A1 (en) 2003-02-21 2004-08-26 Phil Hetherington Method and apparatus for suppressing wind noise
US6798886B1 (en) 1998-10-29 2004-09-28 Paul Reed Smith Guitars, Limited Partnership Method of signal shredding
US20040196989A1 (en) 2003-04-04 2004-10-07 Sol Friedman Method and apparatus for expanding audio data
US6810273B1 (en) 1999-11-15 2004-10-26 Nokia Mobile Phones Noise suppression
US20040263636A1 (en) 2003-06-26 2004-12-30 Microsoft Corporation System and method for distributed meetings
US20050025263A1 (en) 2003-07-23 2005-02-03 Gin-Der Wu Nonlinear overlap method for time scaling
US20050049864A1 (en) 2003-08-29 2005-03-03 Alfred Kaltenmeier Intelligent acoustic microphone fronted with speech recognizing feedback
US20050060142A1 (en) 2003-09-12 2005-03-17 Erik Visser Separation of target acoustic signals in a multi-transducer arrangement
US6882736B2 (en) 2000-09-13 2005-04-19 Siemens Audiologische Technik Gmbh Method for operating a hearing aid or hearing aid system, and a hearing aid and hearing aid system
JP2005110127A (en) 2003-10-01 2005-04-21 Canon Inc Wind noise detecting device and video camera with wind noise detecting device
JP2005148274A (en) 2003-11-13 2005-06-09 Matsushita Electric Ind Co Ltd Signal analyzing method and signal composing method for complex index modulation filter bank, and program therefor and recording medium therefor
JP2005518118A (en) 2002-02-13 2005-06-16 オーディエンス・インコーポレーテッドAudience Incorporated Filter set for frequency analysis
JP2004533155T5 (en) 2005-06-23
US20050152559A1 (en) 2001-12-04 2005-07-14 Stefan Gierl Method for supressing surrounding noise in a hands-free device and hands-free device
JP2005195955A (en) 2004-01-08 2005-07-21 Toshiba Corp Device and method for noise suppression
US20050185813A1 (en) 2004-02-24 2005-08-25 Microsoft Corporation Method and apparatus for multi-sensory speech enhancement on a mobile device
US6944510B1 (en) 1999-05-21 2005-09-13 Koninklijke Philips Electronics N.V. Audio signal time scale modification
US20050203735A1 (en) * 2004-03-09 2005-09-15 International Business Machines Corporation Signal noise reduction
US20050213778A1 (en) 2004-03-17 2005-09-29 Markus Buck System for detecting and reducing noise via a microphone array
US20050238238A1 (en) 2002-07-19 2005-10-27 Li-Qun Xu Method and system for classification of semantic content of audio/video data
US20050276423A1 (en) 1999-03-19 2005-12-15 Roland Aubauer Method and device for receiving and treating audiosignals in surroundings affected by noise
US20050288923A1 (en) 2004-06-25 2005-12-29 The Hong Kong University Of Science And Technology Speech enhancement by noise masking
US6982377B2 (en) 2003-12-18 2006-01-03 Texas Instruments Incorporated Time-scale modification of music signals based on polyphase filterbanks and constrained time-domain processing
US6999582B1 (en) 1999-03-26 2006-02-14 Zarlink Semiconductor Inc. Echo cancelling/suppression for handsets
US7016507B1 (en) * 1997-04-16 2006-03-21 Ami Semiconductor Inc. Method and apparatus for noise reduction particularly in hearing aids
US7020605B2 (en) 2000-09-15 2006-03-28 Mindspeed Technologies, Inc. Speech coding system with time-domain noise attenuation
US20060074646A1 (en) 2004-09-28 2006-04-06 Clarity Technologies, Inc. Method of cascading noise reduction algorithms to avoid speech distortion
US20060072768A1 (en) 1999-06-24 2006-04-06 Schwartz Stephen R Complementary-pair equalizer
US7031478B2 (en) 2000-05-26 2006-04-18 Koninklijke Philips Electronics N.V. Method for noise suppression in an adaptive beamformer
US20060098809A1 (en) 2004-10-26 2006-05-11 Harman Becker Automotive Systems - Wavemakers, Inc. Periodic signal enhancement system
US7054452B2 (en) 2000-08-24 2006-05-30 Sony Corporation Signal processing apparatus and signal processing method
US20060120537A1 (en) 2004-08-06 2006-06-08 Burnett Gregory C Noise suppressing multi-microphone headset
US7065485B1 (en) 2002-01-09 2006-06-20 At&T Corp Enhancing speech intelligibility using variable-rate time-scale modification
US20060133621A1 (en) 2004-12-22 2006-06-22 Broadcom Corporation Wireless telephone having multiple microphones
US7072834B2 (en) 2002-04-05 2006-07-04 Intel Corporation Adapting to adverse acoustic environment in speech processing using playback training data
US20060149535A1 (en) 2004-12-30 2006-07-06 Lg Electronics Inc. Method for controlling speed of audio signals
US7076315B1 (en) 2000-03-24 2006-07-11 Audience, Inc. Efficient computation of log-frequency-scale digital filter cascade
US20060160581A1 (en) 2002-12-20 2006-07-20 Christopher Beaugeant Echo suppression for compressed speech with only partial transcoding of the uplink user data stream
US20060165202A1 (en) 2004-12-21 2006-07-27 Trevor Thomas Signal processor for robust pattern recognition
US7092529B2 (en) 2002-11-01 2006-08-15 Nanyang Technological University Adaptive control system for noise cancellation
US7092882B2 (en) 2000-12-06 2006-08-15 Ncr Corporation Noise suppression in beam-steered microphone array
US20060184363A1 (en) 2005-02-17 2006-08-17 Mccree Alan Noise suppression
US20060198542A1 (en) 2003-02-27 2006-09-07 Abdellatif Benjelloun Touimi Method for the treatment of compressed sound data for spatialization
US20060222184A1 (en) 2004-09-23 2006-10-05 Markus Buck Multi-channel adaptive speech signal processing system with noise reduction
US7146316B2 (en) 2002-10-17 2006-12-05 Clarity Technologies, Inc. Noise reduction in subbanded speech signals
US7155019B2 (en) 2000-03-14 2006-12-26 Apherma Corporation Adaptive microphone matching in multi-microphone directional system
US7164620B2 (en) 2002-10-08 2007-01-16 Nec Corporation Array device and mobile terminal
US20070021958A1 (en) 2005-07-22 2007-01-25 Erik Visser Robust separation of speech signals in a noisy environment
US20070027685A1 (en) 2005-07-27 2007-02-01 Nec Corporation Noise suppression system, method and program
US7174022B1 (en) 2002-11-15 2007-02-06 Fortemedia, Inc. Small array microphone for beam-forming and noise suppression
US20070033020A1 (en) 2003-02-27 2007-02-08 Kelleher Francois Holly L Estimation of noise in a speech signal
US20070067166A1 (en) 2003-09-17 2007-03-22 Xingde Pan Method and device of multi-resolution vector quantilization for audio encoding and decoding
US20070078649A1 (en) 2003-02-21 2007-04-05 Hetherington Phillip A Signature noise removal
US7206418B2 (en) 2001-02-12 2007-04-17 Fortemedia, Inc. Noise suppression for a wireless communication device
US7209567B1 (en) 1998-07-09 2007-04-24 Purdue Research Foundation Communication system with adaptive noise suppression
US20070094031A1 (en) 2005-10-20 2007-04-26 Broadcom Corporation Audio time scale modification using decimation-based synchronized overlap-add algorithm
US20070100612A1 (en) 2005-09-16 2007-05-03 Per Ekstrand Partially complex modulated filter bank
US20070116300A1 (en) 2004-12-22 2007-05-24 Broadcom Corporation Channel decoding for wireless telephones with multiple microphones and multiple description transmission
US7225001B1 (en) 2000-04-24 2007-05-29 Telefonaktiebolaget Lm Ericsson (Publ) System and method for distributed noise suppression
US20070150268A1 (en) 2005-12-22 2007-06-28 Microsoft Corporation Spatial noise suppression for a microphone array
US20070154031A1 (en) 2006-01-05 2007-07-05 Audience, Inc. System and method for utilizing inter-microphone level differences for speech enhancement
US7242762B2 (en) 2002-06-24 2007-07-10 Freescale Semiconductor, Inc. Monitoring and control of an adaptive filter in a communication system
US7246058B2 (en) 2001-05-30 2007-07-17 Aliph, Inc. Detecting voiced and unvoiced speech using both acoustic and nonacoustic sensors
US20070165879A1 (en) 2006-01-13 2007-07-19 Vimicro Corporation Dual Microphone System and Method for Enhancing Voice Quality
US7254242B2 (en) 2002-06-17 2007-08-07 Alpine Electronics, Inc. Acoustic signal processing apparatus and method, and audio device
US20070195968A1 (en) 2006-02-07 2007-08-23 Jaber Associates, L.L.C. Noise suppression method and system with single microphone
US20070230712A1 (en) 2004-09-07 2007-10-04 Koninklijke Philips Electronics, N.V. Telephony Device with Improved Noise Suppression
US20070276656A1 (en) 2006-05-25 2007-11-29 Audience, Inc. System and method for processing an audio signal
US20080019548A1 (en) 2006-01-30 2008-01-24 Audience, Inc. System and method for utilizing omni-directional microphones for speech enhancement
US20080033723A1 (en) * 2006-08-03 2008-02-07 Samsung Electronics Co., Ltd. Speech detection method, medium, and system
US20080071540A1 (en) * 2006-09-13 2008-03-20 Honda Motor Co., Ltd. Speech recognition method for robot under motor noise thereof
US20080140391A1 (en) 2006-12-08 2008-06-12 Micro-Star Int'l Co., Ltd Method for Varying Speech Speed
US20080228478A1 (en) 2005-06-15 2008-09-18 Qnx Software Systems (Wavemakers), Inc. Targeted speech
US20080260175A1 (en) 2002-02-05 2008-10-23 Mh Acoustics, Llc Dual-Microphone Spatial Noise Suppression
JP4184400B2 (en) 2006-10-06 2008-11-19 誠 植村 How to build underground structures
US20090012783A1 (en) 2007-07-06 2009-01-08 Audience, Inc. System and method for adaptive intelligent noise suppression
US20090012786A1 (en) 2007-07-06 2009-01-08 Texas Instruments Incorporated Adaptive Noise Cancellation
US20090080632A1 (en) 2007-09-25 2009-03-26 Microsoft Corporation Spatial audio conferencing
US20090129610A1 (en) 2007-11-15 2009-05-21 Samsung Electronics Co., Ltd. Method and apparatus for canceling noise from mixed sound
US7555075B2 (en) 2006-04-07 2009-06-30 Freescale Semiconductor, Inc. Adjustable noise suppression system
US7555434B2 (en) 2002-07-19 2009-06-30 Nec Corporation Audio decoding device, decoding method, and program
US20090220107A1 (en) 2008-02-29 2009-09-03 Audience, Inc. System and method for providing single microphone noise suppression fallback
US20090228272A1 (en) 2007-11-12 2009-09-10 Tobias Herbig System for distinguishing desired audio signals from noise
US20090238373A1 (en) 2008-03-18 2009-09-24 Audience, Inc. System and method for envelope-based acoustic echo cancellation
US20090253418A1 (en) 2005-06-30 2009-10-08 Jorma Makinen System for conference call and corresponding devices, method and program products
US20090271187A1 (en) 2008-04-25 2009-10-29 Kuan-Chieh Yen Two microphone noise reduction system
US20090296958A1 (en) 2006-07-03 2009-12-03 Nec Corporation Noise suppression method, device, and program
US20090323982A1 (en) 2006-01-30 2009-12-31 Ludger Solbach System and method for providing noise suppression utilizing null processing noise subtraction
US7664640B2 (en) 2002-03-28 2010-02-16 Qinetiq Limited System for estimating parameters of a gaussian mixture model
US20100094643A1 (en) 2006-05-25 2010-04-15 Audience, Inc. Systems and methods for reconstructing decomposed audio signals
US20100166199A1 (en) * 2006-10-26 2010-07-01 Parrot Acoustic echo reduction circuit for a "hands-free" device usable with a cell phone
US7783481B2 (en) * 2003-12-03 2010-08-24 Fujitsu Limited Noise reduction apparatus and noise reducing method
US20100278352A1 (en) 2007-05-25 2010-11-04 Nicolas Petit Wind Suppression/Replacement Component for use with Electronic Systems
US20100282045A1 (en) 2009-05-06 2010-11-11 Ching-Wei Chen Apparatus and method for determining a prominent tempo of an audio work
US20110035213A1 (en) * 2007-06-22 2011-02-10 Vladimir Malenovsky Method and Device for Sound Activity Detection and Sound Signal Classification
US7949522B2 (en) * 2003-02-21 2011-05-24 Qnx Software Systems Co. System for suppressing rain noise
US7953596B2 (en) * 2006-03-01 2011-05-31 Parrot Societe Anonyme Method of denoising a noisy signal including speech and noise components
US20110178800A1 (en) 2010-01-19 2011-07-21 Lloyd Watts Distortion Measurement for Noise Suppression System
US20110182436A1 (en) 2010-01-26 2011-07-28 Carlo Murgia Adaptive Noise Reduction Using Level Cues
US8010355B2 (en) * 2006-04-26 2011-08-30 Zarlink Semiconductor Inc. Low complexity noise reduction method
US8098812B2 (en) 2006-02-22 2012-01-17 Alcatel Lucent Method of controlling an adaptation of a filter
US20120093341A1 (en) 2010-10-19 2012-04-19 Electronics And Telecommunications Research Institute Apparatus and method for separating sound source
US20120121096A1 (en) 2010-11-12 2012-05-17 Apple Inc. Intelligibility control using ambient noise detection
US20120140917A1 (en) 2010-06-04 2012-06-07 Apple Inc. Active noise cancellation decisions using a degraded reference
US8280731B2 (en) * 2007-03-19 2012-10-02 Dolby Laboratories Licensing Corporation Noise variance estimator for speech enhancement
JP5053587B2 (en) 2006-07-31 2012-10-17 東亞合成株式会社 High-purity production method of alkali metal hydroxide
US8363850B2 (en) 2007-06-13 2013-01-29 Kabushiki Kaisha Toshiba Audio signal processing method and apparatus for the same

Patent Citations (283)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004531767T5 (en) 2008-04-17
JP2004533155T5 (en) 2005-06-23
US3976863A (en) 1974-07-01 1976-08-24 Alfred Engel Optimal decoder for non-stationary signals
US3978287A (en) 1974-12-11 1976-08-31 Nasa Real time analysis of voiced sounds
US4137510A (en) 1976-01-22 1979-01-30 Victor Company Of Japan, Ltd. Frequency band dividing filter
US4516259A (en) 1981-05-11 1985-05-07 Kokusai Denshin Denwa Co., Ltd. Speech analysis-synthesis system
US4433604A (en) 1981-09-22 1984-02-28 Texas Instruments Incorporated Frequency domain digital encoding technique for musical signals
US4535473A (en) * 1981-10-31 1985-08-13 Tokyo Shibaura Denki Kabushiki Kaisha Apparatus for detecting the duration of voice
US4536844A (en) 1983-04-26 1985-08-20 Fairchild Camera And Instrument Corporation Method and apparatus for simulating aural response information
US5054085A (en) 1983-05-18 1991-10-01 Speech Systems, Inc. Preprocessing system for speech recognition
US4674125A (en) 1983-06-27 1987-06-16 Rca Corporation Real-time hierarchal pyramid signal processing apparatus
US4581758A (en) 1983-11-04 1986-04-08 At&T Bell Laboratories Acoustic direction identification system
US5150413A (en) 1984-03-23 1992-09-22 Ricoh Company, Ltd. Extraction of phonemic information
US4649505A (en) 1984-07-02 1987-03-10 General Electric Company Two-input crosstalk-resistant adaptive noise canceller
US4718104A (en) 1984-11-27 1988-01-05 Rca Corporation Filter-subtract-decimate hierarchical pyramid signal analyzing and synthesizing technique
US4630304A (en) 1985-07-01 1986-12-16 Motorola, Inc. Automatic background noise estimator for a noise suppression system
US4628529A (en) 1985-07-01 1986-12-09 Motorola, Inc. Noise suppression system
US4658426A (en) 1985-10-10 1987-04-14 Harold Antin Adaptive noise suppressor
US4920508A (en) 1986-05-22 1990-04-24 Inmos Limited Multistage digital signal multiplication and addition
US4812996A (en) 1986-11-26 1989-03-14 Tektronix, Inc. Signal viewing instrumentation control system
US4811404A (en) 1987-10-01 1989-03-07 Motorola, Inc. Noise suppression system
US4864620A (en) 1987-12-21 1989-09-05 The Dsp Group, Inc. Method for performing time-scale modification of speech information or speech signals
US5027410A (en) 1988-11-10 1991-06-25 Wisconsin Alumni Research Foundation Adaptive, programmable signal processing and filtering for hearing aids
US5099738A (en) 1989-01-03 1992-03-31 Hotz Instruments Technology, Inc. MIDI musical translator
US5208864A (en) 1989-03-10 1993-05-04 Nippon Telegraph & Telephone Corporation Method of detecting acoustic signal
US5187776A (en) 1989-06-16 1993-02-16 International Business Machines Corp. Image editor zoom function
US5289273A (en) 1989-09-20 1994-02-22 Semborg-Recrob, Corp. Animated character system with real-time control
US5341432A (en) 1989-10-06 1994-08-23 Matsushita Electric Industrial Co., Ltd. Apparatus and method for performing speech rate modification and improved fidelity
US5142961A (en) 1989-11-07 1992-09-01 Fred Paroutaud Method and apparatus for stimulation of acoustic musical instruments
US5319736A (en) 1989-12-06 1994-06-07 National Research Council Of Canada System for separating speech from background noise
US5058419A (en) 1990-04-10 1991-10-22 Earl H. Ruble Method and apparatus for determining the location of a sound source
US5230022A (en) 1990-06-22 1993-07-20 Clarion Co., Ltd. Low frequency compensating circuit for audio signals
US5119711A (en) 1990-11-01 1992-06-09 International Business Machines Corporation Midi file translation
US5224170A (en) 1991-04-15 1993-06-29 Hewlett-Packard Company Time domain compensation for transducer mismatch
US5210366A (en) 1991-06-10 1993-05-11 Sykes Jr Richard O Method and device for detecting and separating voices in a complex musical composition
US5175769A (en) 1991-07-23 1992-12-29 Rolm Systems Method for time-scale modification of signals
US5479564A (en) 1991-08-09 1995-12-26 U.S. Philips Corporation Method and apparatus for manipulating pitch and/or duration of a signal
US5473702A (en) 1992-06-03 1995-12-05 Oki Electric Industry Co., Ltd. Adaptive noise canceller
US5381512A (en) 1992-06-24 1995-01-10 Moscom Corporation Method and apparatus for speech feature recognition based on models of auditory signal processing
US5402496A (en) 1992-07-13 1995-03-28 Minnesota Mining And Manufacturing Company Auditory prosthesis, noise suppression apparatus and feedback suppression apparatus having focused adaptive filtering
US6061456A (en) 1992-10-29 2000-05-09 Andrea Electronics Corporation Noise cancellation apparatus
US5381473A (en) 1992-10-29 1995-01-10 Andrea Electronics Corporation Noise cancellation apparatus
US5402493A (en) 1992-11-02 1995-03-28 Central Institute For The Deaf Electronic simulator of non-linear and active cochlear spectrum analysis
US5323459A (en) 1992-11-10 1994-06-21 Nec Corporation Multi-channel echo canceler
US5502663A (en) 1992-12-14 1996-03-26 Apple Computer, Inc. Digital filter having independent damping and frequency parameters
US5400409A (en) 1992-12-23 1995-03-21 Daimler-Benz Ag Noise-reduction method for noise-affected voice channels
US5473759A (en) 1993-02-22 1995-12-05 Apple Computer, Inc. Sound analysis and resynthesis using correlograms
US5590241A (en) 1993-04-30 1996-12-31 Motorola Inc. Speech processing system and method for enhancing a speech signal in a noisy environment
US5583784A (en) 1993-05-14 1996-12-10 Fraunhofer-Gesellschaft Zur Forderung Der Angewandten Forschung E.V. Frequency analysis method
US5602962A (en) 1993-09-07 1997-02-11 U.S. Philips Corporation Mobile radio set comprising a speech processing arrangement
US5675778A (en) 1993-10-04 1997-10-07 Fostex Corporation Of America Method and apparatus for audio editing incorporating visual comparison
US5574824A (en) 1994-04-11 1996-11-12 The United States Of America As Represented By The Secretary Of The Air Force Analysis/synthesis-based microphone array speech enhancer with variable signal distortion
US5471195A (en) 1994-05-16 1995-11-28 C & K Systems, Inc. Direction-sensing acoustic glass break detecting system
US5544250A (en) 1994-07-18 1996-08-06 Motorola Noise suppression system and method therefor
US5717829A (en) 1994-07-28 1998-02-10 Sony Corporation Pitch control of memory addressing for changing speed of audio playback
US5729612A (en) 1994-08-05 1998-03-17 Aureal Semiconductor Inc. Method and apparatus for measuring head-related transfer functions
US5943429A (en) 1995-01-30 1999-08-24 Telefonaktiebolaget Lm Ericsson Spectral subtraction noise suppression method
US5682463A (en) 1995-02-06 1997-10-28 Lucent Technologies Inc. Perceptual audio compression based on loudness uncertainty
US5920840A (en) 1995-02-28 1999-07-06 Motorola, Inc. Communication system and method using a speaker dependent time-scaling technique
US5587998A (en) 1995-03-03 1996-12-24 At&T Method and apparatus for reducing residual far-end echo in voice communication networks
US5706395A (en) 1995-04-19 1998-01-06 Texas Instruments Incorporated Adaptive weiner filtering using a dynamic suppression factor
US6263307B1 (en) 1995-04-19 2001-07-17 Texas Instruments Incorporated Adaptive weiner filtering using line spectral frequencies
US6180273B1 (en) 1995-08-30 2001-01-30 Honda Giken Kogyo Kabushiki Kaisha Fuel cell with cooling medium circulation arrangement and method
US5809463A (en) 1995-09-15 1998-09-15 Hughes Electronics Method of detecting double talk in an echo canceller
US5694474A (en) 1995-09-18 1997-12-02 Interval Research Corporation Adaptive filter for signal processing and method therefor
US6002776A (en) 1995-09-18 1999-12-14 Interval Research Corporation Directional acoustic signal processor and method therefor
US5792971A (en) 1995-09-29 1998-08-11 Opcode Systems, Inc. Method and system for editing digital audio information with music-like parameters
US5845243A (en) * 1995-10-13 1998-12-01 U.S. Robotics Mobile Communications Corp. Method and apparatus for wavelet based data compression having adaptive bit rate control for compression of audio information
US6108626A (en) 1995-10-27 2000-08-22 Cselt-Centro Studi E Laboratori Telecomunicazioni S.P.A. Object oriented audio coding
US5956674A (en) 1995-12-01 1999-09-21 Digital Theater Systems, Inc. Multi-channel predictive subband audio coder using psychoacoustic adaptive bit allocation in frequency, time and over the multiple channels
US5974380A (en) 1995-12-01 1999-10-26 Digital Theater Systems, Inc. Multi-channel audio decoder
US5839101A (en) 1995-12-12 1998-11-17 Nokia Mobile Phones Ltd. Noise suppressor and method for suppressing background noise in noisy speech, and a mobile station
US5732189A (en) 1995-12-22 1998-03-24 Lucent Technologies Inc. Audio signal coding with a signal adaptive filterbank
US5757937A (en) 1996-01-31 1998-05-26 Nippon Telegraph And Telephone Corporation Acoustic noise suppressor
US5749064A (en) 1996-03-01 1998-05-05 Texas Instruments Incorporated Method and system for time scale modification utilizing feature vectors about zero crossing points
US5825320A (en) 1996-03-19 1998-10-20 Sony Corporation Gain control method for audio encoding device
US20010031053A1 (en) 1996-06-19 2001-10-18 Feng Albert S. Binaural signal processing techniques
US6978159B2 (en) 1996-06-19 2005-12-20 Board Of Trustees Of The University Of Illinois Binaural signal processing using multiple acoustic sensors and digital filtering
US6222927B1 (en) 1996-06-19 2001-04-24 The University Of Illinois Binaural signal processing system and method
US6072881A (en) 1996-07-08 2000-06-06 Chiefs Voice Incorporated Microphone noise rejection system
US5796819A (en) 1996-07-24 1998-08-18 Ericsson Inc. Echo canceller for non-linear circuits
US5806025A (en) 1996-08-07 1998-09-08 U S West, Inc. Method and system for adaptive filtering of speech signals using signal-to-noise ratio to choose subband filter bank
US6140809A (en) 1996-08-09 2000-10-31 Advantest Corporation Spectrum analyzer
US20030072382A1 (en) 1996-08-29 2003-04-17 Cisco Systems, Inc. Spatio-temporal processing for communication
US6097820A (en) 1996-12-23 2000-08-01 Lucent Technologies Inc. System and method for suppressing noise in digitally represented voice signals
US5978824A (en) 1997-01-29 1999-11-02 Nec Corporation Noise canceler
US5933495A (en) 1997-02-07 1999-08-03 Texas Instruments Incorporated Subband acoustic noise suppression
US7016507B1 (en) * 1997-04-16 2006-03-21 Ami Semiconductor Inc. Method and apparatus for noise reduction particularly in hearing aids
US5983139A (en) 1997-05-01 1999-11-09 Med-El Elektromedizinische Gerate Ges.M.B.H. Cochlear implant system
US6529606B1 (en) 1997-05-16 2003-03-04 Motorola, Inc. Method and system for reducing undesired signals in a communication environment
US6795558B2 (en) 1997-06-26 2004-09-21 Fujitsu Limited Microphone array apparatus
US20020041693A1 (en) 1997-06-26 2002-04-11 Naoshi Matsuo Microphone array apparatus
US20020106092A1 (en) 1997-06-26 2002-08-08 Naoshi Matsuo Microphone array apparatus
US20020080980A1 (en) 1997-06-26 2002-06-27 Naoshi Matsuo Microphone array apparatus
US6317501B1 (en) 1997-06-26 2001-11-13 Fujitsu Limited Microphone array apparatus
US6760450B2 (en) 1997-06-26 2004-07-06 Fujitsu Limited Microphone array apparatus
US6137349A (en) 1997-07-02 2000-10-24 Micronas Intermetall Gmbh Filter combination for sampling rate conversion
US6430295B1 (en) 1997-07-11 2002-08-06 Telefonaktiebolaget Lm Ericsson (Publ) Methods and apparatus for measuring signal level and delay at multiple sensors
US6449586B1 (en) 1997-08-01 2002-09-10 Nec Corporation Control method of adaptive array and adaptive array apparatus
US6216103B1 (en) 1997-10-20 2001-04-10 Sony Corporation Method for implementing a speech recognition system to determine speech endpoints during conditions with background noise
US6134524A (en) * 1997-10-24 2000-10-17 Nortel Networks Corporation Method and apparatus to detect and delimit foreground speech
US20020002455A1 (en) 1998-01-09 2002-01-03 At&T Corporation Core estimator and adaptive gains from signal to noise ratio in a hybrid speech enhancement system
US6321193B1 (en) * 1998-01-27 2001-11-20 Telefonaktiebolaget Lm Ericsson Distance and distortion estimation method and apparatus in channel optimized vector quantization
US6343267B1 (en) 1998-04-30 2002-01-29 Matsushita Electric Industrial Co., Ltd. Dimensionality reduction for speaker normalization and speaker and environment adaptation using eigenvoice techniques
US6717991B1 (en) 1998-05-27 2004-04-06 Telefonaktiebolaget Lm Ericsson (Publ) System and method for dual microphone signal noise reduction using spectral subtraction
US6748095B1 (en) 1998-06-23 2004-06-08 Worldcom, Inc. Headset with multiple connections
US5990405A (en) 1998-07-08 1999-11-23 Gibson Guitar Corp. System and method for generating and controlling a simulated musical concert experience
US7209567B1 (en) 1998-07-09 2007-04-24 Purdue Research Foundation Communication system with adaptive noise suppression
US6453289B1 (en) * 1998-07-24 2002-09-17 Hughes Electronics Corporation Method of noise reduction for speech codecs
US6339758B1 (en) 1998-07-31 2002-01-15 Kabushiki Kaisha Toshiba Noise suppress processing apparatus and method
US6173255B1 (en) 1998-08-18 2001-01-09 Lockheed Martin Corporation Synchronized overlap add voice processing using windows and one bit correlators
US6223090B1 (en) 1998-08-24 2001-04-24 The United States Of America As Represented By The Secretary Of The Air Force Manikin positioning for acoustic measuring
US6122610A (en) 1998-09-23 2000-09-19 Verance Corporation Noise suppression for low bitrate speech coder
US6798886B1 (en) 1998-10-29 2004-09-28 Paul Reed Smith Guitars, Limited Partnership Method of signal shredding
US6469732B1 (en) 1998-11-06 2002-10-22 Vtel Corporation Acoustic source location using a microphone array
US6205422B1 (en) * 1998-11-30 2001-03-20 Microsoft Corporation Morphological pure speech detection using valley percentage
US6266633B1 (en) 1998-12-22 2001-07-24 Itt Manufacturing Enterprises Noise suppression and channel equalization preprocessor for speech and speaker recognizers: method and apparatus
US6381570B2 (en) 1999-02-12 2002-04-30 Telogy Networks, Inc. Adaptive two-threshold method for discriminating noise from speech in a communication signal
US6363345B1 (en) 1999-02-18 2002-03-26 Andrea Electronics Corporation System, method and apparatus for cancelling noise
US20050276423A1 (en) 1999-03-19 2005-12-15 Roland Aubauer Method and device for receiving and treating audiosignals in surroundings affected by noise
US6999582B1 (en) 1999-03-26 2006-02-14 Zarlink Semiconductor Inc. Echo cancelling/suppression for handsets
US6487257B1 (en) 1999-04-12 2002-11-26 Telefonaktiebolaget L M Ericsson Signal noise reduction by time-domain spectral subtraction using fixed filters
US20010016020A1 (en) 1999-04-12 2001-08-23 Harald Gustafsson System and method for dual microphone signal noise reduction using spectral subtraction
US6496795B1 (en) 1999-05-05 2002-12-17 Microsoft Corporation Modulated complex lapped transform for integrated signal enhancement and coding
US6944510B1 (en) 1999-05-21 2005-09-13 Koninklijke Philips Electronics N.V. Audio signal time scale modification
US6219408B1 (en) 1999-05-28 2001-04-17 Paul Kurth Apparatus and method for simultaneously transmitting biomedical data and human voice over conventional telephone lines
US6226616B1 (en) 1999-06-21 2001-05-01 Digital Theater Systems, Inc. Sound quality of established low bit-rate audio coding systems without loss of decoder compatibility
US20060072768A1 (en) 1999-06-24 2006-04-06 Schwartz Stephen R Complementary-pair equalizer
US6355869B1 (en) 1999-08-19 2002-03-12 Duane Mitton Method and system for creating musical scores from musical recordings
US6738482B1 (en) 1999-09-27 2004-05-18 Jaber Associates, Llc Noise suppression system with dual microphone echo cancellation
US20050027520A1 (en) 1999-11-15 2005-02-03 Ville-Veikko Mattila Noise suppression
US6810273B1 (en) 1999-11-15 2004-10-26 Nokia Mobile Phones Noise suppression
US7171246B2 (en) 1999-11-15 2007-01-30 Nokia Mobile Phones Ltd. Noise suppression
US6513004B1 (en) 1999-11-24 2003-01-28 Matsushita Electric Industrial Co., Ltd. Optimized local feature extraction for automatic speech recognition
US6549630B1 (en) 2000-02-04 2003-04-15 Plantronics, Inc. Signal expander with discrimination between close and distant acoustic source
US7155019B2 (en) 2000-03-14 2006-12-26 Apherma Corporation Adaptive microphone matching in multi-microphone directional system
US20010038699A1 (en) 2000-03-20 2001-11-08 Audia Technology, Inc. Automatic directional processing control for multi-microphone system
US7076315B1 (en) 2000-03-24 2006-07-11 Audience, Inc. Efficient computation of log-frequency-scale digital filter cascade
US6434417B1 (en) 2000-03-28 2002-08-13 Cardiac Pacemakers, Inc. Method and system for detecting cardiac depolarization
US20020009203A1 (en) 2000-03-31 2002-01-24 Gamze Erten Method and apparatus for voice signal extraction
US6516066B2 (en) 2000-04-11 2003-02-04 Nec Corporation Apparatus for detecting direction of sound source and turning microphone toward sound source
US7225001B1 (en) 2000-04-24 2007-05-29 Telefonaktiebolaget Lm Ericsson (Publ) System and method for distributed noise suppression
US20030138116A1 (en) 2000-05-10 2003-07-24 Jones Douglas L. Interference suppression techniques
US7031478B2 (en) 2000-05-26 2006-04-18 Koninklijke Philips Electronics N.V. Method for noise suppression in an adaptive beamformer
US6622030B1 (en) 2000-06-29 2003-09-16 Ericsson Inc. Echo suppression using adaptive gain based on residual echo energy
US20040133421A1 (en) 2000-07-19 2004-07-08 Burnett Gregory C. Voice activity detector (VAD) -based multiple-microphone acoustic noise suppression
US6718309B1 (en) 2000-07-26 2004-04-06 Ssi Corporation Continuously variable time scale modification of digital audio signals
US7054452B2 (en) 2000-08-24 2006-05-30 Sony Corporation Signal processing apparatus and signal processing method
US6882736B2 (en) 2000-09-13 2005-04-19 Siemens Audiologische Technik Gmbh Method for operating a hearing aid or hearing aid system, and a hearing aid and hearing aid system
US7020605B2 (en) 2000-09-15 2006-03-28 Mindspeed Technologies, Inc. Speech coding system with time-domain noise attenuation
US20020116187A1 (en) 2000-10-04 2002-08-22 Gamze Erten Speech detection
US7092882B2 (en) 2000-12-06 2006-08-15 Ncr Corporation Noise suppression in beam-steered microphone array
US20020133334A1 (en) 2001-02-02 2002-09-19 Geert Coorman Time scale modification of digitally sampled waveforms in the time domain
US7617099B2 (en) 2001-02-12 2009-11-10 FortMedia Inc. Noise suppression by two-channel tandem spectrum modification for speech signal in an automobile
US7206418B2 (en) 2001-02-12 2007-04-17 Fortemedia, Inc. Noise suppression for a wireless communication device
US20030040908A1 (en) 2001-02-12 2003-02-27 Fortemedia, Inc. Noise suppression for speech signal in an automobile
US6915264B2 (en) 2001-02-22 2005-07-05 Lucent Technologies Inc. Cochlear filter bank structure for determining masked thresholds for use in perceptual audio coding
US20020147595A1 (en) 2001-02-22 2002-10-10 Frank Baumgarte Cochlear filter bank structure for determining masked thresholds for use in perceptual audio coding
US20030033140A1 (en) 2001-04-05 2003-02-13 Rakesh Taori Time-scale modification of signals
US7412379B2 (en) 2001-04-05 2008-08-12 Koninklijke Philips Electronics N.V. Time-scale modification of signals
US20020184013A1 (en) 2001-04-20 2002-12-05 Alcatel Method of masking noise modulation and disturbing noise in voice communication
US20030014248A1 (en) 2001-04-27 2003-01-16 Csem, Centre Suisse D'electronique Et De Microtechnique Sa Method and system for enhancing speech in a noisy environment
US20040131178A1 (en) 2001-05-14 2004-07-08 Mark Shahaf Telephone apparatus and a communication method using such apparatus
US7246058B2 (en) 2001-05-30 2007-07-17 Aliph, Inc. Detecting voiced and unvoiced speech using both acoustic and nonacoustic sensors
US20030128851A1 (en) 2001-06-06 2003-07-10 Satoru Furuta Noise suppressor
US20030039369A1 (en) 2001-07-04 2003-02-27 Bullen Robert Bruce Environmental noise monitoring
US20030072460A1 (en) 2001-07-17 2003-04-17 Clarity Llc Directional sound acquisition
US7142677B2 (en) 2001-07-17 2006-11-28 Clarity Technologies, Inc. Directional sound acquisition
US6584203B2 (en) 2001-07-18 2003-06-24 Agere Systems Inc. Second-order adaptive differential microphone array
US20030026437A1 (en) 2001-07-20 2003-02-06 Janse Cornelis Pieter Sound reinforcement system having an multi microphone echo suppressor as post processor
US20030063759A1 (en) 2001-08-08 2003-04-03 Brennan Robert L. Directional audio signal processing using an oversampled filterbank
US7359520B2 (en) 2001-08-08 2008-04-15 Dspfactory Ltd. Directional audio signal processing using an oversampled filterbank
US20030061032A1 (en) 2001-09-24 2003-03-27 Clarity, Llc Selective sound enhancement
US20030101048A1 (en) 2001-10-30 2003-05-29 Chunghwa Telecom Co., Ltd. Suppression system of background noise of voice sounds signals and the method thereof
US6792118B2 (en) 2001-11-14 2004-09-14 Applied Neurosystems Corporation Computation of multi-sensor time delays
US20030095667A1 (en) 2001-11-14 2003-05-22 Applied Neurosystems Corporation Computation of multi-sensor time delays
US20030099345A1 (en) 2001-11-27 2003-05-29 Siemens Information Telephone having improved hands free operation audio quality and method of operation thereof
US6785381B2 (en) 2001-11-27 2004-08-31 Siemens Information And Communication Networks, Inc. Telephone having improved hands free operation audio quality and method of operation thereof
US20030103632A1 (en) 2001-12-03 2003-06-05 Rafik Goubran Adaptive sound masking system and method
US20050152559A1 (en) 2001-12-04 2005-07-14 Stefan Gierl Method for supressing surrounding noise in a hands-free device and hands-free device
US7065485B1 (en) 2002-01-09 2006-06-20 At&T Corp Enhancing speech intelligibility using variable-rate time-scale modification
US7171008B2 (en) 2002-02-05 2007-01-30 Mh Acoustics, Llc Reducing noise in audio systems
US20030147538A1 (en) 2002-02-05 2003-08-07 Mh Acoustics, Llc, A Delaware Corporation Reducing noise in audio systems
US20080260175A1 (en) 2002-02-05 2008-10-23 Mh Acoustics, Llc Dual-Microphone Spatial Noise Suppression
US20050228518A1 (en) 2002-02-13 2005-10-13 Applied Neurosystems Corporation Filter set for frequency analysis
JP2005518118A (en) 2002-02-13 2005-06-16 オーディエンス・インコーポレーテッドAudience Incorporated Filter set for frequency analysis
US20050216259A1 (en) 2002-02-13 2005-09-29 Applied Neurosystems Corporation Filter set for frequency analysis
US20030169891A1 (en) 2002-03-08 2003-09-11 Ryan Jim G. Low-noise directional microphone system
US20040013276A1 (en) 2002-03-22 2004-01-22 Ellis Richard Thompson Analog audio signal enhancement system using a noise suppression algorithm
US20030228023A1 (en) 2002-03-27 2003-12-11 Burnett Gregory C. Microphone and Voice Activity Detection (VAD) configurations for use with communication systems
US7664640B2 (en) 2002-03-28 2010-02-16 Qinetiq Limited System for estimating parameters of a gaussian mixture model
US7072834B2 (en) 2002-04-05 2006-07-04 Intel Corporation Adapting to adverse acoustic environment in speech processing using playback training data
US7254242B2 (en) 2002-06-17 2007-08-07 Alpine Electronics, Inc. Acoustic signal processing apparatus and method, and audio device
US7242762B2 (en) 2002-06-24 2007-07-10 Freescale Semiconductor, Inc. Monitoring and control of an adaptive filter in a communication system
US7555434B2 (en) 2002-07-19 2009-06-30 Nec Corporation Audio decoding device, decoding method, and program
JP2004053895A (en) 2002-07-19 2004-02-19 Matsushita Electric Ind Co Ltd Device and method for audio decoding, and program
US20050238238A1 (en) 2002-07-19 2005-10-27 Li-Qun Xu Method and system for classification of semantic content of audio/video data
US20040078199A1 (en) 2002-08-20 2004-04-22 Hanoh Kremer Method for auditory based noise reduction and an apparatus for auditory based noise reduction
US20040047464A1 (en) 2002-09-11 2004-03-11 Zhuliang Yu Adaptive noise cancelling microphone system
US6917688B2 (en) 2002-09-11 2005-07-12 Nanyang Technological University Adaptive noise cancelling microphone system
US20040057574A1 (en) 2002-09-20 2004-03-25 Christof Faller Suppression of echo signals and the like
US7164620B2 (en) 2002-10-08 2007-01-16 Nec Corporation Array device and mobile terminal
US7146316B2 (en) 2002-10-17 2006-12-05 Clarity Technologies, Inc. Noise reduction in subbanded speech signals
US7092529B2 (en) 2002-11-01 2006-08-15 Nanyang Technological University Adaptive control system for noise cancellation
US7174022B1 (en) 2002-11-15 2007-02-06 Fortemedia, Inc. Small array microphone for beam-forming and noise suppression
US20060160581A1 (en) 2002-12-20 2006-07-20 Christopher Beaugeant Echo suppression for compressed speech with only partial transcoding of the uplink user data stream
US20070078649A1 (en) 2003-02-21 2007-04-05 Hetherington Phillip A Signature noise removal
US20040165736A1 (en) 2003-02-21 2004-08-26 Phil Hetherington Method and apparatus for suppressing wind noise
US7949522B2 (en) * 2003-02-21 2011-05-24 Qnx Software Systems Co. System for suppressing rain noise
US20060198542A1 (en) 2003-02-27 2006-09-07 Abdellatif Benjelloun Touimi Method for the treatment of compressed sound data for spatialization
US20070033020A1 (en) 2003-02-27 2007-02-08 Kelleher Francois Holly L Estimation of noise in a speech signal
US20040196989A1 (en) 2003-04-04 2004-10-07 Sol Friedman Method and apparatus for expanding audio data
US20040263636A1 (en) 2003-06-26 2004-12-30 Microsoft Corporation System and method for distributed meetings
US20050025263A1 (en) 2003-07-23 2005-02-03 Gin-Der Wu Nonlinear overlap method for time scaling
US20050049864A1 (en) 2003-08-29 2005-03-03 Alfred Kaltenmeier Intelligent acoustic microphone fronted with speech recognizing feedback
US7099821B2 (en) 2003-09-12 2006-08-29 Softmax, Inc. Separation of target acoustic signals in a multi-transducer arrangement
US20050060142A1 (en) 2003-09-12 2005-03-17 Erik Visser Separation of target acoustic signals in a multi-transducer arrangement
US20070067166A1 (en) 2003-09-17 2007-03-22 Xingde Pan Method and device of multi-resolution vector quantilization for audio encoding and decoding
JP2005110127A (en) 2003-10-01 2005-04-21 Canon Inc Wind noise detecting device and video camera with wind noise detecting device
JP2005148274A (en) 2003-11-13 2005-06-09 Matsushita Electric Ind Co Ltd Signal analyzing method and signal composing method for complex index modulation filter bank, and program therefor and recording medium therefor
US7433907B2 (en) 2003-11-13 2008-10-07 Matsushita Electric Industrial Co., Ltd. Signal analyzing method, signal synthesizing method of complex exponential modulation filter bank, program thereof and recording medium thereof
US7783481B2 (en) * 2003-12-03 2010-08-24 Fujitsu Limited Noise reduction apparatus and noise reducing method
US6982377B2 (en) 2003-12-18 2006-01-03 Texas Instruments Incorporated Time-scale modification of music signals based on polyphase filterbanks and constrained time-domain processing
JP2005195955A (en) 2004-01-08 2005-07-21 Toshiba Corp Device and method for noise suppression
US20050185813A1 (en) 2004-02-24 2005-08-25 Microsoft Corporation Method and apparatus for multi-sensory speech enhancement on a mobile device
US20050203735A1 (en) * 2004-03-09 2005-09-15 International Business Machines Corporation Signal noise reduction
US20050213778A1 (en) 2004-03-17 2005-09-29 Markus Buck System for detecting and reducing noise via a microphone array
US20050288923A1 (en) 2004-06-25 2005-12-29 The Hong Kong University Of Science And Technology Speech enhancement by noise masking
US20080201138A1 (en) 2004-07-22 2008-08-21 Softmax, Inc. Headset for Separation of Speech Signals in a Noisy Environment
US20060120537A1 (en) 2004-08-06 2006-06-08 Burnett Gregory C Noise suppressing multi-microphone headset
US20070230712A1 (en) 2004-09-07 2007-10-04 Koninklijke Philips Electronics, N.V. Telephony Device with Improved Noise Suppression
US20060222184A1 (en) 2004-09-23 2006-10-05 Markus Buck Multi-channel adaptive speech signal processing system with noise reduction
US20060074646A1 (en) 2004-09-28 2006-04-06 Clarity Technologies, Inc. Method of cascading noise reduction algorithms to avoid speech distortion
US7383179B2 (en) 2004-09-28 2008-06-03 Clarity Technologies, Inc. Method of cascading noise reduction algorithms to avoid speech distortion
US20060098809A1 (en) 2004-10-26 2006-05-11 Harman Becker Automotive Systems - Wavemakers, Inc. Periodic signal enhancement system
US20060165202A1 (en) 2004-12-21 2006-07-27 Trevor Thomas Signal processor for robust pattern recognition
US20060133621A1 (en) 2004-12-22 2006-06-22 Broadcom Corporation Wireless telephone having multiple microphones
US20070116300A1 (en) 2004-12-22 2007-05-24 Broadcom Corporation Channel decoding for wireless telephones with multiple microphones and multiple description transmission
US20060149535A1 (en) 2004-12-30 2006-07-06 Lg Electronics Inc. Method for controlling speed of audio signals
US20060184363A1 (en) 2005-02-17 2006-08-17 Mccree Alan Noise suppression
US20080228478A1 (en) 2005-06-15 2008-09-18 Qnx Software Systems (Wavemakers), Inc. Targeted speech
US20090253418A1 (en) 2005-06-30 2009-10-08 Jorma Makinen System for conference call and corresponding devices, method and program products
US20070021958A1 (en) 2005-07-22 2007-01-25 Erik Visser Robust separation of speech signals in a noisy environment
US20070027685A1 (en) 2005-07-27 2007-02-01 Nec Corporation Noise suppression system, method and program
US20070100612A1 (en) 2005-09-16 2007-05-03 Per Ekstrand Partially complex modulated filter bank
US20070094031A1 (en) 2005-10-20 2007-04-26 Broadcom Corporation Audio time scale modification using decimation-based synchronized overlap-add algorithm
US20070150268A1 (en) 2005-12-22 2007-06-28 Microsoft Corporation Spatial noise suppression for a microphone array
US20070154031A1 (en) 2006-01-05 2007-07-05 Audience, Inc. System and method for utilizing inter-microphone level differences for speech enhancement
US20070165879A1 (en) 2006-01-13 2007-07-19 Vimicro Corporation Dual Microphone System and Method for Enhancing Voice Quality
US20080019548A1 (en) 2006-01-30 2008-01-24 Audience, Inc. System and method for utilizing omni-directional microphones for speech enhancement
US20090323982A1 (en) 2006-01-30 2009-12-31 Ludger Solbach System and method for providing noise suppression utilizing null processing noise subtraction
US20070195968A1 (en) 2006-02-07 2007-08-23 Jaber Associates, L.L.C. Noise suppression method and system with single microphone
US8098812B2 (en) 2006-02-22 2012-01-17 Alcatel Lucent Method of controlling an adaptation of a filter
US7953596B2 (en) * 2006-03-01 2011-05-31 Parrot Societe Anonyme Method of denoising a noisy signal including speech and noise components
US7555075B2 (en) 2006-04-07 2009-06-30 Freescale Semiconductor, Inc. Adjustable noise suppression system
US8010355B2 (en) * 2006-04-26 2011-08-30 Zarlink Semiconductor Inc. Low complexity noise reduction method
US20100094643A1 (en) 2006-05-25 2010-04-15 Audience, Inc. Systems and methods for reconstructing decomposed audio signals
US20070276656A1 (en) 2006-05-25 2007-11-29 Audience, Inc. System and method for processing an audio signal
US20090296958A1 (en) 2006-07-03 2009-12-03 Nec Corporation Noise suppression method, device, and program
JP5053587B2 (en) 2006-07-31 2012-10-17 東亞合成株式会社 High-purity production method of alkali metal hydroxide
US20080033723A1 (en) * 2006-08-03 2008-02-07 Samsung Electronics Co., Ltd. Speech detection method, medium, and system
US20080071540A1 (en) * 2006-09-13 2008-03-20 Honda Motor Co., Ltd. Speech recognition method for robot under motor noise thereof
JP4184400B2 (en) 2006-10-06 2008-11-19 誠 植村 How to build underground structures
US20100166199A1 (en) * 2006-10-26 2010-07-01 Parrot Acoustic echo reduction circuit for a "hands-free" device usable with a cell phone
US20080140391A1 (en) 2006-12-08 2008-06-12 Micro-Star Int'l Co., Ltd Method for Varying Speech Speed
US8280731B2 (en) * 2007-03-19 2012-10-02 Dolby Laboratories Licensing Corporation Noise variance estimator for speech enhancement
US20100278352A1 (en) 2007-05-25 2010-11-04 Nicolas Petit Wind Suppression/Replacement Component for use with Electronic Systems
US8363850B2 (en) 2007-06-13 2013-01-29 Kabushiki Kaisha Toshiba Audio signal processing method and apparatus for the same
US20110035213A1 (en) * 2007-06-22 2011-02-10 Vladimir Malenovsky Method and Device for Sound Activity Detection and Sound Signal Classification
US20090012786A1 (en) 2007-07-06 2009-01-08 Texas Instruments Incorporated Adaptive Noise Cancellation
US20090012783A1 (en) 2007-07-06 2009-01-08 Audience, Inc. System and method for adaptive intelligent noise suppression
US20090080632A1 (en) 2007-09-25 2009-03-26 Microsoft Corporation Spatial audio conferencing
US20090228272A1 (en) 2007-11-12 2009-09-10 Tobias Herbig System for distinguishing desired audio signals from noise
US20090129610A1 (en) 2007-11-15 2009-05-21 Samsung Electronics Co., Ltd. Method and apparatus for canceling noise from mixed sound
US20090220107A1 (en) 2008-02-29 2009-09-03 Audience, Inc. System and method for providing single microphone noise suppression fallback
US20090238373A1 (en) 2008-03-18 2009-09-24 Audience, Inc. System and method for envelope-based acoustic echo cancellation
US20090271187A1 (en) 2008-04-25 2009-10-29 Kuan-Chieh Yen Two microphone noise reduction system
US20100282045A1 (en) 2009-05-06 2010-11-11 Ching-Wei Chen Apparatus and method for determining a prominent tempo of an audio work
US20110178800A1 (en) 2010-01-19 2011-07-21 Lloyd Watts Distortion Measurement for Noise Suppression System
US20110182436A1 (en) 2010-01-26 2011-07-28 Carlo Murgia Adaptive Noise Reduction Using Level Cues
US20120140917A1 (en) 2010-06-04 2012-06-07 Apple Inc. Active noise cancellation decisions using a degraded reference
US20120093341A1 (en) 2010-10-19 2012-04-19 Electronics And Telecommunications Research Institute Apparatus and method for separating sound source
US20120121096A1 (en) 2010-11-12 2012-05-17 Apple Inc. Intelligibility control using ambient noise detection

Non-Patent Citations (75)

* Cited by examiner, † Cited by third party
Title
"ENT 172." Instructional Module. Prince George's Community College Department of Engineering Technology. Accessed: Oct. 15, 2011. Subsection: "Polar and Rectangular Notation". .
"ENT 172." Instructional Module. Prince George's Community College Department of Engineering Technology. Accessed: Oct. 15, 2011. Subsection: "Polar and Rectangular Notation". <http://academic.ppgcc.edu/ent/ent172—instr—mod.html>.
Allen, Jont B. "Short Term Spectral Analysis, Synthesis, and Modification by Discrete Fourier Transform", IEEE Transactions on Acoustics, Speech, and Signal Processing. vol. ASSP-25, No. 3, Jun. 1977. pp. 235-238.
Allen, Jont B. et al. "A Unified Approach to Short-Time Fourier Analysis and Synthesis", Proceedings of the IEEE. vol. 65, No. 11, Nov. 1977. pp. 1558-1564.
Avendano, Carlos, "Frequency-Domain Source Identification and Manipulation in Stereo Mixes for Enhancement, Suppression and Re-Panning Applications," 2003 IEEE Workshop on Application of Signal Processing to Audio and Acoustics, Oct. 19-22, pp. 55-58, New Paltz, New York, USA.
Bach et al, Learning Spectral Clustering with application to spech separation, Journal of machine learning research, 2006.
Boll, Steven F. "Suppression of Acoustic Noise in Speech Using Spectral Subtraction", Dept. of Computer Science, University of Utah Salt Lake City, Utah, Apr. 1979, pp. 18-19.
Boll, Steven F. "Suppression of Acoustic Noise in Speech using Spectral Subtraction", IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-27, No. 2, Apr. 1979, pp. 113-120.
Boll, Steven F. et al. "Suppression of Acoustic Noise in Speech Using Two Microphone Adaptive Noise Cancellation", IEEE Transactions on Acoustic, Speech, and Signal Processing, vol. ASSP-28, No. 6, Dec. 1980, pp. 752-753.
Chen, Jingdong et al. "New Insights into the Noise Reduction Wiener Filter", IEEE Transactions on Audio, Speech, and Language Processing. vol. 14, No. 4, Jul. 2006, pp. 1218-1234.
Cohen, Israel et al. "Microphone Array Post-Filtering for Non-Stationary Noise Suppression", IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2002, pp. 1-4.
Cohen, Israel, "Multichannel Post-Filtering in Nonstationary Noise Environments", IEEE Transactions on Signal Processing, vol. 52, No. 5, May 2004, pp. 1149-1160.
Cosi, Piero et al. (1996), "Lyon's Auditory Model Inversion: a Tool for Sound Separation and Speech Enhancement," Proceedings of ESCA Workshop on ‘The Auditory Basis of Speech Perception,’ Keele University, Keele (UK), Jul. 15-19, 1996, pp. 194-197.
Cosi, Piero et al. (1996), "Lyon's Auditory Model Inversion: a Tool for Sound Separation and Speech Enhancement," Proceedings of ESCA Workshop on 'The Auditory Basis of Speech Perception,' Keele University, Keele (UK), Jul. 15-19, 1996, pp. 194-197.
Dahl, Mattias et al., "Acoustic Echo and Noise Cancelling Using Microphone Arrays", International Symposium on Signal Processing and its Applications, ISSPA, Gold coast, Australia, Aug. 25-30, 1996, pp. 379-382.
Dahl, Mattias et al., "Simultaneous Echo Cancellation and Car Noise Suppression Employing a Microphone Array", 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr. 21-24, pp. 239-242.
Demol, M. et al. "Efficient Non-Uniform Time-Scaling of Speech With WSOLA for CALL Applications", Proceedings of InSTIL/ICALL2004-NLP and Speech Technologies in Advanced Language Learning Systems-Venice Jun. 17-19, 2004.
Demol, M. et al. "Efficient Non-Uniform Time-Scaling of Speech With WSOLA for CALL Applications", Proceedings of InSTIL/ICALL2004—NLP and Speech Technologies in Advanced Language Learning Systems—Venice Jun. 17-19, 2004.
Elko, Gary W., "Chapter 2: Differential Microphone Arrays", "Audio Signal Processing for Next-Generation Multimedia Communication Systems", 2004, pp. 12-65, Kluwer Academic Publishers, Norwell, Massachusetts, USA.
Fast Cochlea Transform, US Trademark Reg. No. 2,875,755 (Aug. 17, 2004).
Fazel et al., An overview of statistical pattern recognition techniques for speaker verification, IEEE, May 2011.
Fuchs, Martin et al. "Noise Suppression for Automotive Applications Based on Directional Information", 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 17-21, pp. 237-240.
Fulghum, D. P. et al., "LPC Voice Digitizer with Background Noise Suppression", 1979 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 220-223.
Goubran, R.A. "Acoustic Noise Suppression Using Regression Adaptive Filtering", 1990 IEEE 40th Vehicular Technology Conference, May 6-9, pp. 48-53.
Graupe, Daniel et al., "Blind Adaptive Filtering of Speech from Noise of Unknown Spectrum Using a Virtual Feedback Configuration", IEEE Transactions on Speech and Audio Processing, Mar. 2000, vol. 8, No. 2, pp. 146-158.
Haykin, Simon et al. "Appendix A.2 Complex Numbers." Signals and Systems. 2nd Ed. 2003. p. 764.
Hermansky, Hynek "Should Recognizers Have Ears?", In Proc. ESCA Tutorial and Research Workshop on Robust Speech Recognition for Unknown Communication Channels, pp. 1-10, France 1997.
Hohmann, V. "Frequency Analysis and Synthesis Using a Gammatone Filterbank", ACTA Acustica United with Acustica, 2002, vol. 88, pp. 433-442.
International Search Report and Written Opinion dated Apr. 9, 2008 in Application No. PCT/US07/21654.
International Search Report and Written Opinion dated Aug. 27, 2009 in Application No. PCT/US09/03813.
International Search Report and Written Opinion dated Mar. 31, 2011 in Application No. PCT/US11/22462.
International Search Report and Written Opinion dated May 11, 2009 in Application No. PCT/US09/01667.
International Search Report and Written Opinion dated May 20, 2010 in Application No. PCT/US09/06754.
International Search Report and Written Opinion dated Oct. 1, 2008 in Application No. PCT/US08/08249.
International Search Report and Written Opinion dated Oct. 19, 2007 in Application No. PCT/US07/00463.
International Search Report and Written Opinion dated Sep. 16, 2008 in Application No. PCT/US07/12628.
International Search Report dated Apr. 3, 2003 in Application No. PCT/US02/36946.
International Search Report dated Jun. 8, 2001 in Application No. PCT/US01/08372.
International Search Report dated May 29, 2003 in Application No. PCT/US03/04124.
Jeffress, Lloyd A. et al. "A Place Theory of Sound Localization," Journal of Comparative and Physiological Psychology, 1948, vol. 41, p. 35-39.
Jeong, Hyuk et al., "Implementation of a New Algorithm Using the STFT with Variable Frequency Resolution for the Time-Frequency Auditory Model", J. Audio Eng. Soc., Apr. 1999, vol. 47, No. 4., pp. 240-251.
Kates, James M. "A Time-Domain Digital Cochlear Model", IEEE Transactions on Signal Processing, Dec. 1991, vol. 39, No. 12, pp. 2573-2592.
Klautau et al, Discriminative Gaussian Mixture Models a Comparison with Kernel Classifiers, ICML, 2003.
Laroche, Jean. "Time and Pitch Scale Modification of Audio Signals", in "Applications of Digital Signal Processing to Audio and Acoustics", The Kluwer International Series in Engineering and Computer Science, vol. 437, pp. 279-309, 2002.
Lazzaro, John et al., "A Silicon Model of Auditory Localization," Neural Computation Spring 1989, vol. 1, pp. 47-57, Massachusetts Institute of Technology.
Lippmann, Richard P. "Speech Recognition by Machines and Humans", Speech Communication, Jul. 1997, vol. 22, No. 1, pp. 1-15.
Liu, Chen et al. "A Two-Microphone Dual Delay-Line Approach for Extraction of a Speech Sound in the Presence of Multiple Interferers", Journal of the Acoustical Society of America, vol. 110, No. 6, Dec. 2001, pp. 3218-3231.
Martin, Rainer "Spectral Subtraction Based on Minimum Statistics", in Proceedings Europe. Signal Processing Conf., 1994, pp. 1182-1185.
Martin, Rainer et al. "Combined Acoustic Echo Cancellation, Dereverberation and Noise Reduction: A two Microphone Approach", Annales des Telecommunications/Annals of Telecommunications. vol. 49, No. 7-8, Jul.-Aug. 1994, pp. 429-438.
Mitra, Sanjit K. Digital Signal Processing: a Computer-based Approach. 2nd Ed. 2001. pp. 131-133.
Mizumachi, Mitsunori et al. "Noise Reduction by Paired-Microphones Using Spectral Subtraction", 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, May 12-15. pp. 1001-1004.
Moonen, Marc et al. "Multi-Microphone Signal Enhancement Techniques for Noise Suppression and Dereverbration," http://www.esat.kuleuven.ac.be/sista/yearreport97//node37.html, accessed on Apr. 21, 1998.
Moulines, Eric et al., "Non-Parametric Techniques for Pitch-Scale and Time-Scale Modification of Speech", Speech Communication, vol. 16, pp. 175-205, 1995.
Parra, Lucas et al. "Convolutive Blind Separation of Non-Stationary Sources", IEEE Transactions on Speech and Audio Processing. vol. 8, No. 3, May 2008, pp. 320-327.
Rabiner, Lawrence R. et al. "Digital Processing of Speech Signals", (Prentice-Hall Series in Signal Processing). Upper Saddle River, NJ: Prentice Hall, 1978.
Ron Weiss and Daniel P. W. Ellis, Estimating single-channel source separation masks: relevance vector machine classifiers vs. pitch-based masking. Workshop on Statistical and Perceptual Audio Processing, 2006. *
Schimmel, Steven et al., "Coherent Envelope Detection for Modulation Filtering of Speech," 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, No. 7, pp. 221-224.
Slaney, Malcom, "Lyon's Cochlear Model", Advanced Technology Group, Apple Technical Report #13, Apple Computer, Inc., 1988, pp. 1-79.
Slaney, Malcom, et al. "Auditory Model Inversion for Sound Separation," 1994 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr. 19-22, vol. 2, pp. 77-80.
Slaney, Malcom. "An Introduction to Auditory Model Inversion", Interval Technical Report IRC 1994-014, http://coweb.ecn.purdue.edu/~maclom/interval/1994-014/, Sep. 1994, accessed on Jul. 6, 2010.
Slaney, Malcom. "An Introduction to Auditory Model Inversion", Interval Technical Report IRC 1994-014, http://coweb.ecn.purdue.edu/˜maclom/interval/1994-014/, Sep. 1994, accessed on Jul. 6, 2010.
Solbach, Ludger "An Architecture for Robust Partial Tracking and Onset Localization in Single Channel Audio Signal Mixes", Technical University Hamburg-Harburg, 1998.
Stahl, V. et al., "Quantile Based Noise Estimation for Spectral Subtraction and Wiener Filtering," 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun. 5-9, vol. 3, pp. 1875-1878.
Sundaram et al, Discriminating two types of noise sources using cortical representation and dimension reduction technique, IEE, 2007.
Syntrillium Software Corporation, "Cool Edit User's Manual", 1996, pp. 1-74.
Tashev, Ivan et al. "Microphone Array for Headset with Spatial Noise Suppressor", http://research.microsoft.com/users/ivantash/Documents/Tashev-MAforHeadset-HSCMA-05.pdf. (4 pages).
Tashev, Ivan et al. "Microphone Array for Headset with Spatial Noise Suppressor", http://research.microsoft.com/users/ivantash/Documents/Tashev—MAforHeadset—HSCMA—05.pdf. (4 pages).
Tchorz, Jurgen et al., "SNR Estimation Based on Amplitude Modulation Analysis with Applications to Noise Suppression", IEEE Transactions on Speech and Audio Processing, Vol. 11, No. 3, May 2003, pp. 184-192.
Tognieri et al, a comparison of the LBG,LVQ, MLP,SOM and GMM algorithms for Vector Quantisation and Clustering Analysis, 1992.
Valin, Jean-Marc et al. "Enhanced Robot Audition Based on Microphone Array Source Separation with Post-Filter", Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep. 28-Oct. 2, 2004, Sendai, Japan. pp. 2123-2128.
Verhelst, Werner, "Overlap-Add Methods for Time-Scaling of Speech", Speech Communication vol. 30, pp. 207-221, 2000.
Watts, Lloyd Narrative of Prior Disclosure of Audio Display on Feb. 15, 2000 and May 31, 2000.
Watts, Lloyd, "Robust Hearing Systems for Intelligent Machines," Applied Neurosystems Corporation, 2001, pp. 1-5.
Widrow, B. et al., "Adaptive Antenna Systems," Proceedings of the IEEE, vol. 55, No. 12, pp. 2143-2159, Dec. 1967.
Yoo, Heejong et al., "Continuous-Time Audio Noise Suppression and Real-Time Implementation", 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 13-17, pp. IV3980-IV3983.

Cited By (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9830899B1 (en) 2006-05-25 2017-11-28 Knowles Electronics, Llc Adaptive noise cancellation
US9185500B2 (en) 2008-06-02 2015-11-10 Starkey Laboratories, Inc. Compression of spaced sources for hearing assistance devices
US9332360B2 (en) 2008-06-02 2016-05-03 Starkey Laboratories, Inc. Compression and mixing for hearing assistance devices
US9924283B2 (en) 2008-06-02 2018-03-20 Starkey Laboratories, Inc. Enhanced dynamics processing of streaming audio by source separation and remixing
US20110191101A1 (en) * 2008-08-05 2011-08-04 Christian Uhle Apparatus and Method for Processing an Audio Signal for Speech Enhancement Using a Feature Extraction
US9064498B2 (en) * 2008-08-05 2015-06-23 Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. Apparatus and method for processing an audio signal for speech enhancement using a feature extraction
US9094078B2 (en) * 2009-12-16 2015-07-28 Samsung Electronics Co., Ltd. Method and apparatus for removing noise from input signal in noisy environment
US20110142256A1 (en) * 2009-12-16 2011-06-16 Samsung Electronics Co., Ltd. Method and apparatus for removing noise from input signal in noisy environment
US9699554B1 (en) 2010-04-21 2017-07-04 Knowles Electronics, Llc Adaptive signal equalization
US10218327B2 (en) * 2011-01-10 2019-02-26 Zhinian Jing Dynamic enhancement of audio (DAE) in headset systems
US20120209601A1 (en) * 2011-01-10 2012-08-16 Aliphcom Dynamic enhancement of audio (DAE) in headset systems
US10230346B2 (en) 2011-01-10 2019-03-12 Zhinian Jing Acoustic voice activity detection
US20130260692A1 (en) * 2012-03-29 2013-10-03 Bose Corporation Automobile communication system
US8892046B2 (en) * 2012-03-29 2014-11-18 Bose Corporation Automobile communication system
US20130332157A1 (en) * 2012-06-08 2013-12-12 Apple Inc. Audio noise estimation and audio noise reduction using multiple microphones
US9966067B2 (en) * 2012-06-08 2018-05-08 Apple Inc. Audio noise estimation and audio noise reduction using multiple microphones
US9640194B1 (en) 2012-10-04 2017-05-02 Knowles Electronics, Llc Noise suppression for speech processing based on machine-learning mask estimation
US20140129215A1 (en) * 2012-11-02 2014-05-08 Samsung Electronics Co., Ltd. Electronic device and method for estimating quality of speech signal
US9536540B2 (en) 2013-07-19 2017-01-03 Knowles Electronics, Llc Speech signal separation and synthesis based on auditory scene analysis and speech modeling
EP2858382A1 (en) * 2013-10-01 2015-04-08 Starkey Laboratories, Inc. System and method for selective harmonic enhancement for hearing assistance devices
US9769550B2 (en) 2013-11-06 2017-09-19 Nvidia Corporation Efficient digital microphone receiver process and system
US20150127335A1 (en) * 2013-11-07 2015-05-07 Nvidia Corporation Voice trigger
US9454975B2 (en) * 2013-11-07 2016-09-27 Nvidia Corporation Voice trigger
US9524735B2 (en) 2014-01-31 2016-12-20 Apple Inc. Threshold adaptation in two-channel noise estimation and voice activity detection
US9467779B2 (en) 2014-05-13 2016-10-11 Apple Inc. Microphone partial occlusion detector
US10109290B2 (en) 2014-06-13 2018-10-23 Retune DSP ApS Multi-band noise reduction system and methodology for digital audio signals
WO2015189261A1 (en) * 2014-06-13 2015-12-17 Retune DSP ApS Multi-band noise reduction system and methodology for digital audio signals
US10269368B2 (en) 2014-06-13 2019-04-23 Oticon A/S Audio processing device and a method for estimating a signal-to-noise-ratio of a sound signal
US9799330B2 (en) 2014-08-28 2017-10-24 Knowles Electronics, Llc Multi-sourced noise suppression
WO2016063795A1 (en) * 2014-10-21 2016-04-28 Mitsubishi Electric Corporation Method for transforming a noisy speech signal to an enhanced speech signal
US20170325020A1 (en) * 2014-12-12 2017-11-09 Nuance Communications, Inc. System and method for generating a self-steering beamformer
US10412490B2 (en) 2016-02-25 2019-09-10 Dolby Laboratories Licensing Corporation Multitalker optimised beamforming system and method
US10433076B2 (en) 2016-05-30 2019-10-01 Oticon A/S Audio processing device and a method for estimating a signal-to-noise-ratio of a sound signal

Similar Documents

Publication Publication Date Title
US8571231B2 (en) Suppressing noise in an audio signal
TWI398855B (en) Multiple microphone voice activity detector
DK3190587T3 (en) Noise estimation for noise reduction and echo suppression in personal communication
US5544250A (en) Noise suppression system and method therefor
JP3457293B2 (en) Noise suppression apparatus and noise suppression method
EP1252796B1 (en) System and method for dual microphone signal noise reduction using spectral subtraction
ES2678415T3 (en) Apparatus and procedure for processing and audio signal for speech improvement by using a feature extraction
JP4162604B2 (en) Noise suppression apparatus and noise suppression method
US8194880B2 (en) System and method for utilizing omni-directional microphones for speech enhancement
US8194882B2 (en) System and method for providing single microphone noise suppression fallback
JP3626492B2 (en) Reduction of background noise to improve the quality of the conversation
EP1250703B1 (en) Noise reduction apparatus and method
EP1253581B1 (en) Method and system for speech enhancement in a noisy environment
US8566086B2 (en) System for adaptive enhancement of speech signals
EP1667416A2 (en) Reverberation estimation and suppression system
US20010016020A1 (en) System and method for dual microphone signal noise reduction using spectral subtraction
JP4520732B2 (en) Noise reduction device, and the reduction method
RU2145737C1 (en) Method for noise reduction by means of spectral subtraction
US5706395A (en) Adaptive weiner filtering using a dynamic suppression factor
CN100476949C (en) Multichannel voice detection in adverse environments
US6263307B1 (en) Adaptive weiner filtering using line spectral frequencies
US7464029B2 (en) Robust separation of speech signals in a noisy environment
EP2144232A2 (en) Apparatus and methods for enhancement of speech
US8462958B2 (en) Apparatus and method for computing filter coefficients for echo suppression
US9437180B2 (en) Adaptive noise reduction using level cues

Legal Events

Date Code Title Description
AS Assignment

Owner name: AUDIENCE, INC., CALIFORNIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:EVERY, MARK;KLEIN, DAVID;SIGNING DATES FROM 20080730 TO 20080820;REEL/FRAME:021593/0579

STCF Information on status: patent grant

Free format text: PATENTED CASE

AS Assignment

Owner name: KNOWLES ELECTRONICS, LLC, ILLINOIS

Free format text: MERGER;ASSIGNOR:AUDIENCE LLC;REEL/FRAME:037927/0435

Effective date: 20151221

Owner name: AUDIENCE LLC, CALIFORNIA

Free format text: CHANGE OF NAME;ASSIGNOR:AUDIENCE, INC.;REEL/FRAME:037927/0424

Effective date: 20151217

FPAY Fee payment

Year of fee payment: 4